Overview

Brought to you by YData

Dataset statistics

Number of variables133
Number of observations4920
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows304
Duplicate rows (%)6.2%
Total size in memory5.0 MiB
Average record size in memory1.0 KiB

Variable types

Categorical132
Numeric1

Alerts

fluid_overload has constant value "0" Constant
Dataset has 304 (6.2%) duplicate rowsDuplicates
abdominal_pain is highly overall correlated with dark_urine and 2 other fieldsHigh correlation
abnormal_menstruation is highly overall correlated with brittle_nails and 7 other fieldsHigh correlation
acidity is highly overall correlated with ulcers_on_tongue and 1 other fieldsHigh correlation
acute_liver_failure is highly overall correlated with coma and 1 other fieldsHigh correlation
altered_sensorium is highly overall correlated with weakness_of_one_body_sideHigh correlation
anxiety is highly overall correlated with blurred_and_distorted_vision and 3 other fieldsHigh correlation
back_pain is highly overall correlated with pain_behind_the_eyes and 1 other fieldsHigh correlation
belly_pain is highly overall correlated with constipation and 1 other fieldsHigh correlation
blackheads is highly overall correlated with pus_filled_pimples and 1 other fieldsHigh correlation
bladder_discomfort is highly overall correlated with burning_micturition and 2 other fieldsHigh correlation
blister is highly overall correlated with red_sore_around_nose and 1 other fieldsHigh correlation
blood_in_sputum is highly overall correlated with mild_fever and 2 other fieldsHigh correlation
bloody_stool is highly overall correlated with constipation and 3 other fieldsHigh correlation
blurred_and_distorted_vision is highly overall correlated with anxiety and 9 other fieldsHigh correlation
breathlessness is highly overall correlated with chest_pain and 3 other fieldsHigh correlation
brittle_nails is highly overall correlated with abnormal_menstruation and 8 other fieldsHigh correlation
bruising is highly overall correlated with cramps and 4 other fieldsHigh correlation
burning_micturition is highly overall correlated with bladder_discomfort and 3 other fieldsHigh correlation
chest_pain is highly overall correlated with breathlessness and 2 other fieldsHigh correlation
chills is highly overall correlated with high_fever and 2 other fieldsHigh correlation
cold_hands_and_feets is highly overall correlated with abnormal_menstruation and 8 other fieldsHigh correlation
coma is highly overall correlated with acute_liver_failure and 1 other fieldsHigh correlation
congestion is highly overall correlated with continuous_sneezing and 7 other fieldsHigh correlation
constipation is highly overall correlated with belly_pain and 5 other fieldsHigh correlation
continuous_feel_of_urine is highly overall correlated with bladder_discomfort and 2 other fieldsHigh correlation
continuous_sneezing is highly overall correlated with congestion and 7 other fieldsHigh correlation
cough is highly overall correlated with breathlessness and 2 other fieldsHigh correlation
cramps is highly overall correlated with bruising and 4 other fieldsHigh correlation
dark_urine is highly overall correlated with abdominal_pain and 2 other fieldsHigh correlation
dehydration is highly overall correlated with sunken_eyesHigh correlation
depression is highly overall correlated with brittle_nails and 7 other fieldsHigh correlation
dischromic _patches is highly overall correlated with nodal_skin_eruptionsHigh correlation
distention_of_abdomen is highly overall correlated with fluid_overload.1 and 2 other fieldsHigh correlation
dizziness is highly overall correlated with brittle_nails and 8 other fieldsHigh correlation
drying_and_tingling_lips is highly overall correlated with anxiety and 3 other fieldsHigh correlation
enlarged_thyroid is highly overall correlated with abnormal_menstruation and 8 other fieldsHigh correlation
excessive_hunger is highly overall correlated with blurred_and_distorted_vision and 2 other fieldsHigh correlation
extra_marital_contacts is highly overall correlated with muscle_wasting and 1 other fieldsHigh correlation
family_history is highly overall correlated with mucoid_sputumHigh correlation
fast_heart_rate is highly overall correlated with rusty_sputum and 1 other fieldsHigh correlation
fatigue is highly overall correlated with prognosisHigh correlation
fluid_overload.1 is highly overall correlated with distention_of_abdomen and 2 other fieldsHigh correlation
foul_smell_of urine is highly overall correlated with bladder_discomfort and 2 other fieldsHigh correlation
high_fever is highly overall correlated with chillsHigh correlation
hip_joint_pain is highly overall correlated with knee_pain and 3 other fieldsHigh correlation
history_of_alcohol_consumption is highly overall correlated with distention_of_abdomen and 2 other fieldsHigh correlation
increased_appetite is highly overall correlated with blurred_and_distorted_vision and 4 other fieldsHigh correlation
indigestion is highly overall correlated with internal_itching and 2 other fieldsHigh correlation
inflammatory_nails is highly overall correlated with silver_like_dusting and 2 other fieldsHigh correlation
internal_itching is highly overall correlated with indigestion and 1 other fieldsHigh correlation
irregular_sugar_level is highly overall correlated with blurred_and_distorted_vision and 4 other fieldsHigh correlation
irritability is highly overall correlated with abnormal_menstruation and 4 other fieldsHigh correlation
irritation_in_anus is highly overall correlated with bloody_stool and 3 other fieldsHigh correlation
knee_pain is highly overall correlated with hip_joint_pain and 3 other fieldsHigh correlation
lack_of_concentration is highly overall correlated with dizziness and 1 other fieldsHigh correlation
loss_of_appetite is highly overall correlated with yellowing_of_eyes and 1 other fieldsHigh correlation
loss_of_balance is highly overall correlated with dizziness and 5 other fieldsHigh correlation
loss_of_smell is highly overall correlated with congestion and 7 other fieldsHigh correlation
malaise is highly overall correlated with chills and 3 other fieldsHigh correlation
mild_fever is highly overall correlated with blood_in_sputum and 1 other fieldsHigh correlation
mood_swings is highly overall correlated with abnormal_menstruation and 7 other fieldsHigh correlation
movement_stiffness is highly overall correlated with muscle_weakness and 3 other fieldsHigh correlation
mucoid_sputum is highly overall correlated with family_historyHigh correlation
muscle_wasting is highly overall correlated with extra_marital_contacts and 1 other fieldsHigh correlation
muscle_weakness is highly overall correlated with movement_stiffnessHigh correlation
nausea is highly overall correlated with vomitingHigh correlation
neck_pain is highly overall correlated with hip_joint_pain and 2 other fieldsHigh correlation
nodal_skin_eruptions is highly overall correlated with dischromic _patchesHigh correlation
obesity is highly overall correlated with bruising and 7 other fieldsHigh correlation
pain_behind_the_eyes is highly overall correlated with back_pain and 1 other fieldsHigh correlation
pain_during_bowel_movements is highly overall correlated with bloody_stool and 3 other fieldsHigh correlation
pain_in_anal_region is highly overall correlated with bloody_stool and 3 other fieldsHigh correlation
painful_walking is highly overall correlated with hip_joint_pain and 3 other fieldsHigh correlation
palpitations is highly overall correlated with anxiety and 3 other fieldsHigh correlation
passage_of_gases is highly overall correlated with indigestion and 1 other fieldsHigh correlation
patches_in_throat is highly overall correlated with extra_marital_contacts and 1 other fieldsHigh correlation
phlegm is highly overall correlated with blood_in_sputum and 13 other fieldsHigh correlation
polyuria is highly overall correlated with blurred_and_distorted_vision and 4 other fieldsHigh correlation
prognosis is highly overall correlated with fatigue and 4 other fieldsHigh correlation
prominent_veins_on_calf is highly overall correlated with bruising and 4 other fieldsHigh correlation
puffy_face_and_eyes is highly overall correlated with abnormal_menstruation and 8 other fieldsHigh correlation
pus_filled_pimples is highly overall correlated with blackheads and 1 other fieldsHigh correlation
receiving_blood_transfusion is highly overall correlated with receiving_unsterile_injections and 1 other fieldsHigh correlation
receiving_unsterile_injections is highly overall correlated with receiving_blood_transfusion and 1 other fieldsHigh correlation
red_sore_around_nose is highly overall correlated with blister and 1 other fieldsHigh correlation
red_spots_over_body is highly overall correlated with malaise and 1 other fieldsHigh correlation
redness_of_eyes is highly overall correlated with congestion and 7 other fieldsHigh correlation
restlessness is highly overall correlated with excessive_hunger and 4 other fieldsHigh correlation
runny_nose is highly overall correlated with congestion and 7 other fieldsHigh correlation
rusty_sputum is highly overall correlated with fast_heart_rate and 1 other fieldsHigh correlation
scurring is highly overall correlated with blackheads and 1 other fieldsHigh correlation
shivering is highly overall correlated with continuous_sneezing and 1 other fieldsHigh correlation
silver_like_dusting is highly overall correlated with inflammatory_nails and 2 other fieldsHigh correlation
sinus_pressure is highly overall correlated with congestion and 7 other fieldsHigh correlation
skin_peeling is highly overall correlated with inflammatory_nails and 2 other fieldsHigh correlation
slurred_speech is highly overall correlated with anxiety and 3 other fieldsHigh correlation
small_dents_in_nails is highly overall correlated with inflammatory_nails and 2 other fieldsHigh correlation
spinning_movements is highly overall correlated with loss_of_balance and 2 other fieldsHigh correlation
spotting_ urination is highly overall correlated with burning_micturition and 1 other fieldsHigh correlation
stiff_neck is highly overall correlated with movement_stiffness and 1 other fieldsHigh correlation
stomach_bleeding is highly overall correlated with acute_liver_failure and 1 other fieldsHigh correlation
stomach_pain is highly overall correlated with spotting_ urination and 1 other fieldsHigh correlation
sunken_eyes is highly overall correlated with dehydrationHigh correlation
sweating is highly overall correlated with breathlessness and 1 other fieldsHigh correlation
swelled_lymph_nodes is highly overall correlated with blood_in_sputum and 9 other fieldsHigh correlation
swelling_joints is highly overall correlated with hip_joint_pain and 3 other fieldsHigh correlation
swelling_of_stomach is highly overall correlated with distention_of_abdomen and 2 other fieldsHigh correlation
swollen_blood_vessels is highly overall correlated with bruising and 4 other fieldsHigh correlation
swollen_extremeties is highly overall correlated with abnormal_menstruation and 8 other fieldsHigh correlation
swollen_legs is highly overall correlated with bruising and 4 other fieldsHigh correlation
throat_irritation is highly overall correlated with congestion and 7 other fieldsHigh correlation
toxic_look_(typhos) is highly overall correlated with belly_pain and 1 other fieldsHigh correlation
ulcers_on_tongue is highly overall correlated with acidity and 1 other fieldsHigh correlation
unsteadiness is highly overall correlated with loss_of_balance and 2 other fieldsHigh correlation
visual_disturbances is highly overall correlated with acidity and 4 other fieldsHigh correlation
vomiting is highly overall correlated with nauseaHigh correlation
watering_from_eyes is highly overall correlated with continuous_sneezing and 1 other fieldsHigh correlation
weakness_in_limbs is highly overall correlated with back_pain and 3 other fieldsHigh correlation
weakness_of_one_body_side is highly overall correlated with altered_sensoriumHigh correlation
weight_gain is highly overall correlated with abnormal_menstruation and 8 other fieldsHigh correlation
weight_loss is highly overall correlated with restlessnessHigh correlation
yellow_crust_ooze is highly overall correlated with blister and 1 other fieldsHigh correlation
yellow_urine is highly overall correlated with receiving_blood_transfusion and 1 other fieldsHigh correlation
yellowing_of_eyes is highly overall correlated with abdominal_pain and 4 other fieldsHigh correlation
yellowish_skin is highly overall correlated with abdominal_pain and 3 other fieldsHigh correlation
nodal_skin_eruptions is highly imbalanced (84.8%) Imbalance
continuous_sneezing is highly imbalanced (73.5%) Imbalance
shivering is highly imbalanced (84.8%) Imbalance
stomach_pain is highly imbalanced (73.5%) Imbalance
acidity is highly imbalanced (73.5%) Imbalance
ulcers_on_tongue is highly imbalanced (84.8%) Imbalance
muscle_wasting is highly imbalanced (84.8%) Imbalance
burning_micturition is highly imbalanced (74.0%) Imbalance
spotting_ urination is highly imbalanced (84.8%) Imbalance
weight_gain is highly imbalanced (84.1%) Imbalance
anxiety is highly imbalanced (84.1%) Imbalance
cold_hands_and_feets is highly imbalanced (84.1%) Imbalance
mood_swings is highly imbalanced (72.9%) Imbalance
weight_loss is highly imbalanced (55.5%) Imbalance
restlessness is highly imbalanced (72.9%) Imbalance
lethargy is highly imbalanced (55.5%) Imbalance
patches_in_throat is highly imbalanced (84.8%) Imbalance
irregular_sugar_level is highly imbalanced (84.1%) Imbalance
sunken_eyes is highly imbalanced (84.8%) Imbalance
breathlessness is highly imbalanced (55.9%) Imbalance
dehydration is highly imbalanced (84.8%) Imbalance
indigestion is highly imbalanced (73.5%) Imbalance
pain_behind_the_eyes is highly imbalanced (83.5%) Imbalance
back_pain is highly imbalanced (72.9%) Imbalance
constipation is highly imbalanced (72.9%) Imbalance
mild_fever is highly imbalanced (62.7%) Imbalance
yellow_urine is highly imbalanced (84.1%) Imbalance
acute_liver_failure is highly imbalanced (84.1%) Imbalance
swelling_of_stomach is highly imbalanced (84.1%) Imbalance
swelled_lymph_nodes is highly imbalanced (63.1%) Imbalance
blurred_and_distorted_vision is highly imbalanced (63.6%) Imbalance
phlegm is highly imbalanced (62.7%) Imbalance
throat_irritation is highly imbalanced (83.5%) Imbalance
redness_of_eyes is highly imbalanced (83.5%) Imbalance
sinus_pressure is highly imbalanced (83.5%) Imbalance
runny_nose is highly imbalanced (83.5%) Imbalance
congestion is highly imbalanced (83.5%) Imbalance
weakness_in_limbs is highly imbalanced (84.8%) Imbalance
fast_heart_rate is highly imbalanced (72.4%) Imbalance
pain_during_bowel_movements is highly imbalanced (84.1%) Imbalance
pain_in_anal_region is highly imbalanced (84.1%) Imbalance
bloody_stool is highly imbalanced (84.1%) Imbalance
irritation_in_anus is highly imbalanced (84.1%) Imbalance
neck_pain is highly imbalanced (72.9%) Imbalance
dizziness is highly imbalanced (64.0%) Imbalance
cramps is highly imbalanced (84.1%) Imbalance
bruising is highly imbalanced (84.1%) Imbalance
obesity is highly imbalanced (72.9%) Imbalance
swollen_legs is highly imbalanced (84.1%) Imbalance
swollen_blood_vessels is highly imbalanced (84.8%) Imbalance
puffy_face_and_eyes is highly imbalanced (84.1%) Imbalance
enlarged_thyroid is highly imbalanced (83.5%) Imbalance
brittle_nails is highly imbalanced (83.5%) Imbalance
swollen_extremeties is highly imbalanced (83.5%) Imbalance
excessive_hunger is highly imbalanced (55.1%) Imbalance
extra_marital_contacts is highly imbalanced (84.8%) Imbalance
drying_and_tingling_lips is highly imbalanced (84.1%) Imbalance
slurred_speech is highly imbalanced (83.5%) Imbalance
knee_pain is highly imbalanced (84.1%) Imbalance
hip_joint_pain is highly imbalanced (84.1%) Imbalance
muscle_weakness is highly imbalanced (72.4%) Imbalance
stiff_neck is highly imbalanced (72.9%) Imbalance
swelling_joints is highly imbalanced (72.9%) Imbalance
movement_stiffness is highly imbalanced (84.1%) Imbalance
spinning_movements is highly imbalanced (84.8%) Imbalance
loss_of_balance is highly imbalanced (63.6%) Imbalance
unsteadiness is highly imbalanced (84.1%) Imbalance
weakness_of_one_body_side is highly imbalanced (84.8%) Imbalance
loss_of_smell is highly imbalanced (83.5%) Imbalance
bladder_discomfort is highly imbalanced (84.1%) Imbalance
foul_smell_of urine is highly imbalanced (85.4%) Imbalance
continuous_feel_of_urine is highly imbalanced (84.1%) Imbalance
passage_of_gases is highly imbalanced (84.1%) Imbalance
internal_itching is highly imbalanced (84.1%) Imbalance
toxic_look_(typhos) is highly imbalanced (84.1%) Imbalance
depression is highly imbalanced (72.4%) Imbalance
irritability is highly imbalanced (54.3%) Imbalance
muscle_pain is highly imbalanced (54.3%) Imbalance
altered_sensorium is highly imbalanced (84.1%) Imbalance
red_spots_over_body is highly imbalanced (72.4%) Imbalance
belly_pain is highly imbalanced (84.1%) Imbalance
abnormal_menstruation is highly imbalanced (71.9%) Imbalance
dischromic _patches is highly imbalanced (84.8%) Imbalance
watering_from_eyes is highly imbalanced (84.8%) Imbalance
increased_appetite is highly imbalanced (83.5%) Imbalance
polyuria is highly imbalanced (83.5%) Imbalance
family_history is highly imbalanced (72.9%) Imbalance
mucoid_sputum is highly imbalanced (84.1%) Imbalance
rusty_sputum is highly imbalanced (83.5%) Imbalance
lack_of_concentration is highly imbalanced (84.1%) Imbalance
visual_disturbances is highly imbalanced (84.1%) Imbalance
receiving_blood_transfusion is highly imbalanced (83.5%) Imbalance
receiving_unsterile_injections is highly imbalanced (83.5%) Imbalance
coma is highly imbalanced (83.5%) Imbalance
stomach_bleeding is highly imbalanced (83.5%) Imbalance
distention_of_abdomen is highly imbalanced (84.1%) Imbalance
history_of_alcohol_consumption is highly imbalanced (84.1%) Imbalance
fluid_overload.1 is highly imbalanced (84.1%) Imbalance
blood_in_sputum is highly imbalanced (83.5%) Imbalance
prominent_veins_on_calf is highly imbalanced (84.1%) Imbalance
palpitations is highly imbalanced (83.5%) Imbalance
painful_walking is highly imbalanced (72.9%) Imbalance
pus_filled_pimples is highly imbalanced (84.8%) Imbalance
blackheads is highly imbalanced (84.8%) Imbalance
scurring is highly imbalanced (84.8%) Imbalance
skin_peeling is highly imbalanced (84.1%) Imbalance
silver_like_dusting is highly imbalanced (84.1%) Imbalance
small_dents_in_nails is highly imbalanced (84.1%) Imbalance
inflammatory_nails is highly imbalanced (84.1%) Imbalance
blister is highly imbalanced (84.1%) Imbalance
red_sore_around_nose is highly imbalanced (84.1%) Imbalance
yellow_crust_ooze is highly imbalanced (84.1%) Imbalance
prognosis has 120 (2.4%) zeros Zeros

Reproduction

Analysis started2024-11-13 16:25:13.374542
Analysis finished2024-11-13 16:26:34.435884
Duration1 minute and 21.06 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

itching
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4242 
1
678 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 4242
86.2%
1 678
 
13.8%

Length

2024-11-13T16:26:34.651265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:34.887485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4242
86.2%
1 678
 
13.8%

Most occurring characters

ValueCountFrequency (%)
0 4242
86.2%
1 678
 
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4242
86.2%
1 678
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4242
86.2%
1 678
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4242
86.2%
1 678
 
13.8%

skin_rash
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size38.6 KiB
0.0
4133 
1.0
785 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14754
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 4133
84.0%
1.0 785
 
16.0%
(Missing) 2
 
< 0.1%

Length

2024-11-13T16:26:35.116031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:35.334987image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4133
84.0%
1.0 785
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 9051
61.3%
. 4918
33.3%
1 785
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9051
61.3%
. 4918
33.3%
1 785
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9051
61.3%
. 4918
33.3%
1 785
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9051
61.3%
. 4918
33.3%
1 785
 
5.3%

nodal_skin_eruptions
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:26:35.574212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:35.808364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

continuous_sneezing
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4698 
1
 
222

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Length

2024-11-13T16:26:36.022432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:36.226442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

shivering
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:26:36.460007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:36.686313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

chills
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4122 
1
798 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4122
83.8%
1 798
 
16.2%

Length

2024-11-13T16:26:36.906433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:37.120282image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4122
83.8%
1 798
 
16.2%

Most occurring characters

ValueCountFrequency (%)
0 4122
83.8%
1 798
 
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4122
83.8%
1 798
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4122
83.8%
1 798
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4122
83.8%
1 798
 
16.2%

joint_pain
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4236 
1
684 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4236
86.1%
1 684
 
13.9%

Length

2024-11-13T16:26:37.361845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:37.566352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4236
86.1%
1 684
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0 4236
86.1%
1 684
 
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4236
86.1%
1 684
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4236
86.1%
1 684
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4236
86.1%
1 684
 
13.9%

stomach_pain
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4698 
1
 
222

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Length

2024-11-13T16:26:37.825081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:38.389034image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

acidity
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4698 
1
 
222

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Length

2024-11-13T16:26:38.730304image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:39.139227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

ulcers_on_tongue
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:26:40.150688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:40.547004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

muscle_wasting
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:26:40.988474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:41.409729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

vomiting
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
3006 
1
1914 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3006
61.1%
1 1914
38.9%

Length

2024-11-13T16:26:41.934249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:42.352080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3006
61.1%
1 1914
38.9%

Most occurring characters

ValueCountFrequency (%)
0 3006
61.1%
1 1914
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3006
61.1%
1 1914
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3006
61.1%
1 1914
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3006
61.1%
1 1914
38.9%

burning_micturition
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4704 
1
 
216

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4704
95.6%
1 216
 
4.4%

Length

2024-11-13T16:26:42.588333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:42.797792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4704
95.6%
1 216
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 4704
95.6%
1 216
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4704
95.6%
1 216
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4704
95.6%
1 216
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4704
95.6%
1 216
 
4.4%

spotting_ urination
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:26:43.065692image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:43.275042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

fatigue
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
2988 
1
1932 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2988
60.7%
1 1932
39.3%

Length

2024-11-13T16:26:43.497046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:43.699403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2988
60.7%
1 1932
39.3%

Most occurring characters

ValueCountFrequency (%)
0 2988
60.7%
1 1932
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2988
60.7%
1 1932
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2988
60.7%
1 1932
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2988
60.7%
1 1932
39.3%

weight_gain
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:26:43.946705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:44.187153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

anxiety
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:26:44.407028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:44.611232image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

cold_hands_and_feets
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:26:44.852345image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:45.062468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

mood_swings
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4692 
1
 
228

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Length

2024-11-13T16:26:45.286077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:45.489510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

weight_loss
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4464 
1
456 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4464
90.7%
1 456
 
9.3%

Length

2024-11-13T16:26:45.705328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:45.918131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4464
90.7%
1 456
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 4464
90.7%
1 456
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4464
90.7%
1 456
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4464
90.7%
1 456
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4464
90.7%
1 456
 
9.3%

restlessness
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4692 
1
 
228

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Length

2024-11-13T16:26:46.174265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:47.563561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

lethargy
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4464 
1
456 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4464
90.7%
1 456
 
9.3%

Length

2024-11-13T16:26:47.782563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:47.993315image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4464
90.7%
1 456
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 4464
90.7%
1 456
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4464
90.7%
1 456
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4464
90.7%
1 456
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4464
90.7%
1 456
 
9.3%

patches_in_throat
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:26:48.234009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:48.434115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

irregular_sugar_level
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:26:48.649522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:48.868948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

cough
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4356 
1
564 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4356
88.5%
1 564
 
11.5%

Length

2024-11-13T16:26:49.096931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:49.313327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4356
88.5%
1 564
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0 4356
88.5%
1 564
 
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4356
88.5%
1 564
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4356
88.5%
1 564
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4356
88.5%
1 564
 
11.5%

high_fever
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
3558 
1
1362 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3558
72.3%
1 1362
 
27.7%

Length

2024-11-13T16:26:49.535989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:49.747587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3558
72.3%
1 1362
 
27.7%

Most occurring characters

ValueCountFrequency (%)
0 3558
72.3%
1 1362
 
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3558
72.3%
1 1362
 
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3558
72.3%
1 1362
 
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3558
72.3%
1 1362
 
27.7%

sunken_eyes
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:26:49.977618image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:50.191442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

breathlessness
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4470 
1
450 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4470
90.9%
1 450
 
9.1%

Length

2024-11-13T16:26:50.419387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:50.618545image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4470
90.9%
1 450
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 4470
90.9%
1 450
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4470
90.9%
1 450
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4470
90.9%
1 450
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4470
90.9%
1 450
 
9.1%

sweating
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4242 
1
678 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4242
86.2%
1 678
 
13.8%

Length

2024-11-13T16:26:50.835744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:51.055510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4242
86.2%
1 678
 
13.8%

Most occurring characters

ValueCountFrequency (%)
0 4242
86.2%
1 678
 
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4242
86.2%
1 678
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4242
86.2%
1 678
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4242
86.2%
1 678
 
13.8%

dehydration
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:26:51.307043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:51.511964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

indigestion
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4698 
1
 
222

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Length

2024-11-13T16:26:51.733725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:51.952074image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4698
95.5%
1 222
 
4.5%

headache
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
3786 
1
1134 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3786
77.0%
1 1134
 
23.0%

Length

2024-11-13T16:26:52.171013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:52.401747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3786
77.0%
1 1134
 
23.0%

Most occurring characters

ValueCountFrequency (%)
0 3786
77.0%
1 1134
 
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3786
77.0%
1 1134
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3786
77.0%
1 1134
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3786
77.0%
1 1134
 
23.0%

yellowish_skin
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4008 
1
912 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4008
81.5%
1 912
 
18.5%

Length

2024-11-13T16:26:52.834167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:53.246055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4008
81.5%
1 912
 
18.5%

Most occurring characters

ValueCountFrequency (%)
0 4008
81.5%
1 912
 
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4008
81.5%
1 912
 
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4008
81.5%
1 912
 
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4008
81.5%
1 912
 
18.5%

dark_urine
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4350 
1
570 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4350
88.4%
1 570
 
11.6%

Length

2024-11-13T16:26:53.674130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:54.071915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4350
88.4%
1 570
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 4350
88.4%
1 570
 
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4350
88.4%
1 570
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4350
88.4%
1 570
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4350
88.4%
1 570
 
11.6%

nausea
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
3774 
1
1146 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3774
76.7%
1 1146
 
23.3%

Length

2024-11-13T16:26:54.446976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:54.816347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3774
76.7%
1 1146
 
23.3%

Most occurring characters

ValueCountFrequency (%)
0 3774
76.7%
1 1146
 
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3774
76.7%
1 1146
 
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3774
76.7%
1 1146
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3774
76.7%
1 1146
 
23.3%

loss_of_appetite
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
3768 
1
1152 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3768
76.6%
1 1152
 
23.4%

Length

2024-11-13T16:26:55.283016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:55.721590image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3768
76.6%
1 1152
 
23.4%

Most occurring characters

ValueCountFrequency (%)
0 3768
76.6%
1 1152
 
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3768
76.6%
1 1152
 
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3768
76.6%
1 1152
 
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3768
76.6%
1 1152
 
23.4%

pain_behind_the_eyes
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:26:56.241131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:56.523637image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

back_pain
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4692 
1
 
228

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Length

2024-11-13T16:26:56.744395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:56.967317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

constipation
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4692 
1
 
228

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Length

2024-11-13T16:26:57.187621image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:57.678689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

abdominal_pain
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
3888 
1
1032 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3888
79.0%
1 1032
 
21.0%

Length

2024-11-13T16:26:57.981667image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:58.206136image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3888
79.0%
1 1032
 
21.0%

Most occurring characters

ValueCountFrequency (%)
0 3888
79.0%
1 1032
 
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3888
79.0%
1 1032
 
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3888
79.0%
1 1032
 
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3888
79.0%
1 1032
 
21.0%

diarrhoea
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4356 
1
564 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4356
88.5%
1 564
 
11.5%

Length

2024-11-13T16:26:58.608052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:58.969812image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4356
88.5%
1 564
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0 4356
88.5%
1 564
 
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4356
88.5%
1 564
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4356
88.5%
1 564
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4356
88.5%
1 564
 
11.5%

mild_fever
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4566 
1
 
354

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4566
92.8%
1 354
 
7.2%

Length

2024-11-13T16:26:59.193029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:26:59.504148image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4566
92.8%
1 354
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 4566
92.8%
1 354
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4566
92.8%
1 354
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4566
92.8%
1 354
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4566
92.8%
1 354
 
7.2%

yellow_urine
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:26:59.968837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:00.170706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

yellowing_of_eyes
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4104 
1
816 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4104
83.4%
1 816
 
16.6%

Length

2024-11-13T16:27:00.391906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:00.616759image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4104
83.4%
1 816
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0 4104
83.4%
1 816
 
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4104
83.4%
1 816
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4104
83.4%
1 816
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4104
83.4%
1 816
 
16.6%

acute_liver_failure
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:00.844473image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:01.058971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

fluid_overload
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4920
100.0%

Length

2024-11-13T16:27:01.278273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:01.493755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4920
100.0%

Most occurring characters

ValueCountFrequency (%)
0 4920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4920
100.0%

swelling_of_stomach
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:01.725589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:01.945071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

swelled_lymph_nodes
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4572 
1
 
348

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4572
92.9%
1 348
 
7.1%

Length

2024-11-13T16:27:02.165118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:02.368320image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4572
92.9%
1 348
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 4572
92.9%
1 348
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4572
92.9%
1 348
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4572
92.9%
1 348
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4572
92.9%
1 348
 
7.1%

malaise
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4218 
1
702 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4218
85.7%
1 702
 
14.3%

Length

2024-11-13T16:27:02.580436image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:02.815324image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4218
85.7%
1 702
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 4218
85.7%
1 702
 
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4218
85.7%
1 702
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4218
85.7%
1 702
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4218
85.7%
1 702
 
14.3%

blurred_and_distorted_vision
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4578 
1
 
342

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4578
93.0%
1 342
 
7.0%

Length

2024-11-13T16:27:03.049432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:03.249049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4578
93.0%
1 342
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 4578
93.0%
1 342
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4578
93.0%
1 342
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4578
93.0%
1 342
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4578
93.0%
1 342
 
7.0%

phlegm
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4566 
1
 
354

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4566
92.8%
1 354
 
7.2%

Length

2024-11-13T16:27:03.461733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:03.662012image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4566
92.8%
1 354
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 4566
92.8%
1 354
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4566
92.8%
1 354
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4566
92.8%
1 354
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4566
92.8%
1 354
 
7.2%

throat_irritation
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:03.894145image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:04.096322image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

redness_of_eyes
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:04.311675image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:04.508555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

sinus_pressure
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:04.725459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:04.956608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

runny_nose
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:05.175132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:05.373808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

congestion
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:05.584041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:05.806591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

chest_pain
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4224 
1
696 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4224
85.9%
1 696
 
14.1%

Length

2024-11-13T16:27:06.036480image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:06.242454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4224
85.9%
1 696
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 4224
85.9%
1 696
 
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4224
85.9%
1 696
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4224
85.9%
1 696
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4224
85.9%
1 696
 
14.1%

weakness_in_limbs
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:27:06.488694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:06.748456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

fast_heart_rate
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4686 
1
 
234

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Length

2024-11-13T16:27:07.174704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:07.476116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

pain_during_bowel_movements
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:07.921726image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:08.331327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

pain_in_anal_region
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:08.675152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:09.832338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

bloody_stool
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:10.221456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:10.536014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

irritation_in_anus
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:10.751467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:10.973705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

neck_pain
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4692 
1
 
228

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Length

2024-11-13T16:27:11.200194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:11.399866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

dizziness
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4584 
1
 
336

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4584
93.2%
1 336
 
6.8%

Length

2024-11-13T16:27:11.610127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:11.813140image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4584
93.2%
1 336
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 4584
93.2%
1 336
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4584
93.2%
1 336
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4584
93.2%
1 336
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4584
93.2%
1 336
 
6.8%

cramps
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:12.041278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:12.253113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

bruising
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:12.471018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:12.666469image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

obesity
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4692 
1
 
228

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Length

2024-11-13T16:27:12.903109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:13.107519image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

swollen_legs
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:13.338130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:13.534960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

swollen_blood_vessels
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:27:13.746945image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:13.949726image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

puffy_face_and_eyes
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:14.175002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:14.398095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

enlarged_thyroid
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:14.610749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:14.806648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

brittle_nails
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:15.033050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:15.234127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

swollen_extremeties
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:15.456719image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:15.653288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

excessive_hunger
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4458 
1
462 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4458
90.6%
1 462
 
9.4%

Length

2024-11-13T16:27:15.873535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:16.075246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4458
90.6%
1 462
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 4458
90.6%
1 462
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4458
90.6%
1 462
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4458
90.6%
1 462
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4458
90.6%
1 462
 
9.4%

extra_marital_contacts
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:27:16.291557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:16.509232image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

drying_and_tingling_lips
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:16.719411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:16.925061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

slurred_speech
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:17.153478image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:17.379737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

knee_pain
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:17.618210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:17.817529image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

hip_joint_pain
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:18.047880image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:18.248303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

muscle_weakness
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4686 
1
 
234

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Length

2024-11-13T16:27:18.474857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:18.673491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

stiff_neck
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4692 
1
 
228

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Length

2024-11-13T16:27:18.897367image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:19.101774image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

swelling_joints
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4692 
1
 
228

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Length

2024-11-13T16:27:19.314678image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:19.530443image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

movement_stiffness
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:19.756130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:19.959255image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

spinning_movements
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:27:20.180192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:20.383556image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

loss_of_balance
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4578 
1
 
342

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4578
93.0%
1 342
 
7.0%

Length

2024-11-13T16:27:20.721688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:21.093094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4578
93.0%
1 342
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 4578
93.0%
1 342
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4578
93.0%
1 342
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4578
93.0%
1 342
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4578
93.0%
1 342
 
7.0%

unsteadiness
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:21.567192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:21.978734image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

weakness_of_one_body_side
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:27:22.363057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:22.700057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

loss_of_smell
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:23.105625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:23.538353image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

bladder_discomfort
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:24.034959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:24.339708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

foul_smell_of urine
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4818 
1
 
102

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4818
97.9%
1 102
 
2.1%

Length

2024-11-13T16:27:24.555528image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:24.780768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4818
97.9%
1 102
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 4818
97.9%
1 102
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4818
97.9%
1 102
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4818
97.9%
1 102
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4818
97.9%
1 102
 
2.1%

continuous_feel_of_urine
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:25.014695image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:25.215894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

passage_of_gases
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:25.435175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:25.637501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

internal_itching
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:25.880981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:26.087869image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

toxic_look_(typhos)
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:26.309260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:26.510270image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

depression
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4686 
1
 
234

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Length

2024-11-13T16:27:26.728365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:26.947690image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

irritability
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4446 
1
474 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4446
90.4%
1 474
 
9.6%

Length

2024-11-13T16:27:27.169450image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:27.370176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4446
90.4%
1 474
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 4446
90.4%
1 474
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4446
90.4%
1 474
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4446
90.4%
1 474
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4446
90.4%
1 474
 
9.6%

muscle_pain
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4446 
1
474 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4446
90.4%
1 474
 
9.6%

Length

2024-11-13T16:27:27.591563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:27.818964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4446
90.4%
1 474
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 4446
90.4%
1 474
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4446
90.4%
1 474
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4446
90.4%
1 474
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4446
90.4%
1 474
 
9.6%

altered_sensorium
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:28.046904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:28.253504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

red_spots_over_body
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4686 
1
 
234

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Length

2024-11-13T16:27:28.469134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:28.664951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4686
95.2%
1 234
 
4.8%

belly_pain
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:28.904128image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:29.106743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

abnormal_menstruation
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4680 
1
 
240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4680
95.1%
1 240
 
4.9%

Length

2024-11-13T16:27:29.324054image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:29.525341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4680
95.1%
1 240
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 4680
95.1%
1 240
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4680
95.1%
1 240
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4680
95.1%
1 240
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4680
95.1%
1 240
 
4.9%

dischromic _patches
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:27:29.739973image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:29.958633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

watering_from_eyes
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:27:30.181921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:30.391960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

increased_appetite
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:30.605906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:30.812035image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

polyuria
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:31.060898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:31.267556image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

family_history
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4692 
1
 
228

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Length

2024-11-13T16:27:31.489081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:31.687217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

mucoid_sputum
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:31.912485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:32.151579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

rusty_sputum
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:32.366576image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:32.568462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

lack_of_concentration
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:32.788857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:33.003337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

visual_disturbances
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:33.245903image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:34.281602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

receiving_blood_transfusion
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:34.605484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:34.876237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

receiving_unsterile_injections
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:35.313959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:35.728853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

coma
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:36.089035image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:36.462442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

stomach_bleeding
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:36.828269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:37.259278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

distention_of_abdomen
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:37.738483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:38.168962image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

history_of_alcohol_consumption
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:38.514903image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:38.708025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

fluid_overload.1
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:38.935360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:39.134865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

blood_in_sputum
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:39.372356image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:39.580610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

prominent_veins_on_calf
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:39.808969image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:40.009586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

palpitations
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4800 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Length

2024-11-13T16:27:40.236672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:40.445080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4800
97.6%
1 120
 
2.4%

painful_walking
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4692 
1
 
228

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Length

2024-11-13T16:27:40.662047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:40.855420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4692
95.4%
1 228
 
4.6%

pus_filled_pimples
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:27:41.076844image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:41.280308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

blackheads
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:27:41.503081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:41.703195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

scurring
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4812 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Length

2024-11-13T16:27:41.922130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:42.127249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
97.8%
1 108
 
2.2%

skin_peeling
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:42.348561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:42.570879image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

silver_like_dusting
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:42.824553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:43.031041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

small_dents_in_nails
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:43.257430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:43.486691image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

inflammatory_nails
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:43.702637image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:43.910308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

blister
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:44.140450image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:44.350005image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

red_sore_around_nose
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:44.591392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:44.799653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

yellow_crust_ooze
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
0
4806 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Length

2024-11-13T16:27:45.039203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:27:45.255314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4806
97.7%
1 114
 
2.3%

prognosis
Real number (ℝ)

High correlation  Zeros 

Distinct41
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.780488
Minimum0
Maximum80
Zeros120
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size38.6 KiB
2024-11-13T16:27:45.510949image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q147
median58
Q369
95-th percentile77
Maximum80
Range80
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.426464
Coefficient of variation (CV)0.27168601
Kurtosis2.322684
Mean56.780488
Median Absolute Deviation (MAD)11
Skewness-1.0319108
Sum279360
Variance237.97579
MonotonicityNot monotonic
2024-11-13T16:27:45.865888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
52 120
 
2.4%
58 120
 
2.4%
60 120
 
2.4%
39 120
 
2.4%
75 120
 
2.4%
47 120
 
2.4%
73 120
 
2.4%
50 120
 
2.4%
55 120
 
2.4%
79 120
 
2.4%
Other values (31) 3720
75.6%
ValueCountFrequency (%)
0 120
2.4%
37 120
2.4%
38 120
2.4%
39 120
2.4%
40 120
2.4%
41 120
2.4%
42 120
2.4%
43 120
2.4%
44 120
2.4%
46 120
2.4%
ValueCountFrequency (%)
80 120
2.4%
79 120
2.4%
77 120
2.4%
76 120
2.4%
75 120
2.4%
74 120
2.4%
73 120
2.4%
72 120
2.4%
71 120
2.4%
70 120
2.4%

Interactions

2024-11-13T16:26:31.193772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-13T16:27:46.388312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
abdominal_painabnormal_menstruationacidityacute_liver_failurealtered_sensoriumanxietyback_painbelly_painblackheadsbladder_discomfortblisterblood_in_sputumbloody_stoolblurred_and_distorted_visionbreathlessnessbrittle_nailsbruisingburning_micturitionchest_painchillscold_hands_and_feetscomacongestionconstipationcontinuous_feel_of_urinecontinuous_sneezingcoughcrampsdark_urinedehydrationdepressiondiarrhoeadischromic _patchesdistention_of_abdomendizzinessdrying_and_tingling_lipsenlarged_thyroidexcessive_hungerextra_marital_contactsfamily_historyfast_heart_ratefatiguefluid_overload.1foul_smell_of urineheadachehigh_feverhip_joint_painhistory_of_alcohol_consumptionincreased_appetiteindigestioninflammatory_nailsinternal_itchingirregular_sugar_levelirritabilityirritation_in_anusitchingjoint_painknee_painlack_of_concentrationlethargyloss_of_appetiteloss_of_balanceloss_of_smellmalaisemild_fevermood_swingsmovement_stiffnessmucoid_sputummuscle_painmuscle_wastingmuscle_weaknessnauseaneck_painnodal_skin_eruptionsobesitypain_behind_the_eyespain_during_bowel_movementspain_in_anal_regionpainful_walkingpalpitationspassage_of_gasespatches_in_throatphlegmpolyuriaprognosisprominent_veins_on_calfpuffy_face_and_eyespus_filled_pimplesreceiving_blood_transfusionreceiving_unsterile_injectionsred_sore_around_nosered_spots_over_bodyredness_of_eyesrestlessnessrunny_noserusty_sputumscurringshiveringsilver_like_dustingsinus_pressureskin_peelingskin_rashslurred_speechsmall_dents_in_nailsspinning_movementsspotting_ urinationstiff_neckstomach_bleedingstomach_painsunken_eyessweatingswelled_lymph_nodesswelling_jointsswelling_of_stomachswollen_blood_vesselsswollen_extremetiesswollen_legsthroat_irritationtoxic_look_(typhos)ulcers_on_tongueunsteadinessvisual_disturbancesvomitingwatering_from_eyesweakness_in_limbsweakness_of_one_body_sideweight_gainweight_lossyellow_crust_oozeyellow_urineyellowing_of_eyesyellowish_skin
abdominal_pain1.0000.1150.1100.2970.0760.0760.1110.2770.0740.0760.0760.0790.0760.1390.1620.0790.0760.1080.2080.0700.0760.3050.0790.1410.0760.1100.1840.0760.6640.0740.1130.1520.0740.2770.1380.0760.0790.1640.0740.1110.1130.1490.2770.0720.1530.0540.0760.2770.0790.1310.0760.2770.0760.1670.0760.2620.2680.0760.0760.0150.4850.1390.0790.0450.0740.1110.0760.0760.0190.0740.1130.3600.1110.0740.1110.0790.0760.0760.1110.0790.2770.0740.1420.0790.3030.0760.0760.0740.2860.2860.0760.1130.0790.1110.0790.0790.0740.0740.0760.0790.0760.2230.0790.0760.0740.0740.1110.3050.1100.0740.2050.1400.1110.2770.0740.0790.0760.0790.2770.0740.0760.0760.4790.0740.0740.0740.0760.0150.0760.2770.5260.732
abnormal_menstruation0.1151.0000.0450.0280.0280.0280.0450.0280.0270.0280.0280.0290.0280.0580.0690.6950.0280.0440.0890.0970.6770.0290.0290.0450.0280.0450.0790.0280.0790.0270.4790.2540.0270.0280.3630.0280.6950.2940.0270.0450.4520.2450.0280.0260.1220.1380.0280.0280.0290.0450.0280.0280.0280.6920.0280.0880.0890.0280.0280.2970.1230.0580.0290.0900.0600.9710.0280.0280.0710.0270.4790.1230.0450.0270.0450.0290.0280.0280.0450.0290.0280.0270.0600.0290.4590.0280.6770.0270.0290.0290.0280.0460.0290.4590.0290.0290.0270.0270.0280.0290.0280.0960.0290.0280.0270.0270.0450.0290.0450.0270.2200.0590.0450.0280.0270.6950.0280.0290.0280.0270.0280.0280.1790.0270.0270.0270.6770.2970.0280.0280.0990.106
acidity0.1100.0451.0000.0270.0270.0270.0430.0270.0260.0270.0270.0280.0270.3540.0660.0280.0270.0420.1960.0930.0270.0280.0280.0430.0270.0430.2330.0270.0760.0260.4460.0750.0260.0270.0550.0270.0280.2910.0260.0430.0440.1730.0270.0240.1300.1330.0270.0270.0280.4600.0270.0270.0270.2850.0270.0840.0850.0270.0270.0660.1180.0560.0280.0860.0570.0430.0270.0270.0680.0260.0440.1180.0430.0260.0430.0280.0270.0270.0430.0280.0270.0260.0570.0280.3000.0270.0270.0260.0280.0280.0270.0440.0280.0430.0280.0280.0260.0260.0270.0280.0270.0920.0280.0270.0260.0260.4530.0280.4310.0260.0840.0560.0430.0270.0260.0280.0270.0280.0270.6060.0270.6660.0120.0260.0260.0260.0270.0660.0270.0270.0950.101
acute_liver_failure0.2970.0280.0271.0000.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.9700.0140.0270.0130.0270.0510.0130.3980.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1730.0130.0100.0810.2290.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.3580.0130.0130.0450.2770.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.2580.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3110.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.9700.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1740.0120.0120.0120.0130.0450.0130.0130.3430.300
altered_sensorium0.0760.0280.0270.0131.0000.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.2410.0930.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1580.0120.0120.9130.0130.0450.0130.0130.0650.070
anxiety0.0760.0280.0270.0130.0131.0000.0270.0130.0120.0130.0130.0140.0130.5290.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.9420.0140.4760.0120.0270.0280.1730.0130.0100.2600.0930.0130.0130.0140.0270.0130.0130.0130.4690.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.2580.0270.0120.0270.0140.0130.0130.0270.9700.0130.0120.0380.0140.3110.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.9700.0130.0120.0120.0270.0140.0270.0120.3600.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1740.0120.0120.0120.0130.0450.0130.0130.0650.070
back_pain0.1110.0450.0430.0270.0270.0271.0000.0270.0260.0270.0270.0280.0270.0570.0670.0280.0270.0430.0870.2000.0270.0280.0280.0440.0270.0430.0760.0270.0770.0260.0450.0760.0260.0270.3290.0270.0280.0680.0260.0440.0450.0450.0270.0250.1530.1080.0270.0270.0280.0430.0270.0270.0270.0690.0270.0860.2280.0270.0270.0670.1500.3250.0280.2230.0580.0440.0270.0270.3000.0260.0450.1510.4180.0260.0440.7140.0270.0270.0440.0280.0270.0260.0580.0280.4850.0270.0270.0260.0280.0280.0270.4660.0280.0440.0280.0280.0260.0260.0270.0280.0270.2030.0280.0270.0260.0260.0440.0280.0430.0260.0860.0570.0440.0270.0260.0280.0270.0280.0270.0260.0270.0270.0470.0260.5970.0260.0270.0670.0270.0270.0960.103
belly_pain0.2770.0280.0270.0130.0130.0130.0271.0000.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.3480.0130.0140.0140.6570.0130.0270.0510.0130.0520.0120.0280.4000.0120.0130.0360.0130.0140.0450.0120.0270.0280.1900.0130.0100.2600.2470.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.2580.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.9420.0120.0130.0130.1740.0120.0120.0120.0130.0450.0130.0130.0650.070
blackheads0.0740.0270.0260.0120.0120.0120.0260.0121.0000.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0500.0110.0270.0500.0110.0120.0350.0120.0130.0440.0110.0260.0270.1180.0120.0090.0790.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.0260.0110.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.4540.0120.0120.8820.0130.0130.0120.0270.0130.0260.0130.0130.8820.0110.0120.0130.0120.3190.0130.0120.0110.0110.0260.0130.0260.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1170.0110.0110.0110.0120.0430.0120.0120.0630.068
bladder_discomfort0.0760.0280.0270.0130.0130.0130.0270.0130.0121.0000.0130.0140.0130.0370.0440.0140.0130.6360.0590.0640.0130.0140.0140.0270.9420.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.8830.0810.0930.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
blister0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0131.0000.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.1930.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.9420.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.3290.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.9420.0130.0650.070
blood_in_sputum0.0790.0290.0280.0140.0140.0140.0280.0140.0130.0140.0141.0000.0140.0380.4680.0150.0140.0270.3870.3360.0140.0150.0150.0280.0140.0280.4120.0140.0530.0130.0290.0530.0130.0140.0380.0140.0150.0470.0130.0280.0290.1790.0140.0120.0840.2360.0140.0140.0150.0280.0140.0140.0140.0470.0140.0600.0600.0140.0140.0460.2840.0380.0150.3850.5650.0280.0140.0140.0470.0130.0290.0840.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.5650.0150.3470.0140.0140.0130.0150.0150.0140.0290.0150.0280.0150.0150.0130.0130.0140.0150.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.3700.5700.0280.0140.0130.0150.0140.0150.0140.0130.0140.0140.1800.0130.0130.0130.0140.4650.0140.0140.3530.072
bloody_stool0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0141.0000.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.6570.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.0930.0130.0130.0140.0270.0130.0130.0130.0460.9420.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.9420.9420.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
blurred_and_distorted_vision0.1390.0580.3540.0370.0370.5290.0570.0370.0350.0370.0370.0380.0371.0000.0840.0380.0370.0550.1090.1180.0370.0380.0380.0570.0370.0560.0960.0370.0970.0350.3420.0960.0350.0370.0710.5290.0380.8150.0350.0570.0570.1320.0370.0340.2590.1680.0370.0370.5450.3540.0370.0370.5290.5110.0370.1070.1080.0370.0370.2080.1500.0720.0380.1090.0730.0570.0370.0370.0870.0350.0570.0510.0570.0350.3480.0380.0370.0370.0570.5450.0370.0350.0730.5450.2530.0370.0370.0350.0380.0380.0370.0570.0380.3480.0380.0380.0350.0350.0370.0380.0370.1170.5450.0370.0350.0350.3480.0380.0560.0350.1390.0720.0570.0370.0350.0380.0370.0380.0370.0350.0370.5290.0380.0350.0350.0350.0370.2080.0370.0370.1200.129
breathlessness0.1620.0690.0660.0440.0440.0440.0670.0440.0430.0440.0440.4680.0440.0841.0000.0460.0440.0650.5380.2720.0440.0460.0460.0670.0440.0660.5880.0440.1130.0430.0680.1120.0430.0440.0830.0440.0460.1000.0430.2900.3050.2030.0440.0410.1720.3130.0440.0440.0460.0660.0440.0440.0440.1010.0440.1250.1260.0440.0440.0990.0000.0840.0460.3170.2210.0670.0440.4550.1010.0430.0680.1730.0670.0430.0670.0460.0440.0440.0670.0460.0440.0430.5160.0460.3480.0440.0440.0430.0460.0460.0440.0680.0460.0670.0460.4680.0430.0430.0440.0460.0440.1370.0460.0440.0430.0430.0670.0460.0660.0430.5100.2240.0670.0440.0430.0460.0440.0460.0440.0430.0440.0440.0390.0430.0430.0430.0440.1590.0440.0440.0720.150
brittle_nails0.0790.6950.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.0461.0000.0140.0270.0610.0660.9700.0150.0150.0280.0140.0280.0530.0140.0530.0130.7040.0530.0130.0140.5500.0140.9960.0470.0130.0280.0290.1620.0140.0120.0840.0950.0140.0140.0150.0280.0140.0140.0140.4820.0140.0600.0600.0140.0140.4650.0850.0380.0150.0610.0390.6760.0140.0140.0470.0130.0290.0840.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.0390.0150.3190.0140.9700.0130.0150.0150.0140.0290.0150.0280.0150.0150.0130.0130.0140.0150.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.0600.0390.0280.0140.0130.9960.0140.0150.0140.0130.0140.0140.1240.0130.0130.0130.9700.0460.0140.0140.0670.072
bruising0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0141.0000.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.9420.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1730.0130.0100.0810.0930.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.6570.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.9420.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.9130.0140.9420.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
burning_micturition0.1080.0440.0420.0260.0260.0260.0430.0260.0250.6360.0260.0270.0260.0550.0650.0270.0261.0000.0840.0920.0260.0270.0270.0430.6360.0420.0740.0260.0750.0250.0430.0740.0250.0260.0540.0260.0270.0660.0250.0430.0430.1710.0260.5920.1150.1310.0260.0260.0270.0420.0260.0260.0260.0670.0260.2060.0830.0260.0260.0650.1160.0550.0270.0850.0560.0430.0260.0260.0670.0250.0430.1160.0430.0250.0430.0270.0260.0260.0430.0270.0260.0250.0560.0270.2960.0260.0260.0250.0270.0270.0260.0430.0270.0430.0270.0270.0250.0250.0260.0270.0260.1650.0270.0260.0250.6140.0430.0270.4100.0250.0830.0550.0430.0260.0250.0270.0260.0270.0260.0250.0260.0260.1690.0250.0250.0250.0260.0650.0260.0260.0930.100
chest_pain0.2080.0890.1960.0590.0590.0590.0870.0590.0570.0590.0590.3870.0590.1090.5380.0610.0590.0841.0000.3610.0590.0610.3870.0870.0590.2300.6770.0590.1450.0570.0880.1440.0570.0590.1100.0590.0610.1290.0570.0870.2360.0800.0590.0550.0660.1930.0590.0590.0610.0860.0590.0590.0590.1310.0590.1610.1620.0590.3310.1280.0570.1210.3870.4140.1560.0870.0590.0590.1030.0570.0880.2230.0870.0570.0870.0610.0590.0590.0870.0610.0590.0570.6850.0610.3180.0590.0590.0570.0610.0610.0590.0880.3870.0870.3870.3870.0570.0570.0590.3870.0590.1760.0610.0590.0570.0570.0870.0610.2130.0570.3950.4190.0870.0590.0570.0610.0590.3870.0590.3430.0590.0590.0540.0570.0570.0570.0590.0980.0590.0590.0000.192
chills0.0700.0970.0930.0640.0640.0640.2000.3480.0620.0640.0640.3360.0640.1180.2720.0660.0640.0920.3611.0000.0640.0660.3360.2000.0640.4450.4010.0640.1580.0620.0960.2140.0620.0640.1170.0640.0660.1400.0620.0950.1950.2690.0640.0600.3400.5400.0640.0640.0660.0930.0640.0640.0640.1420.0640.1750.0000.0640.0640.1390.0510.1180.3360.5100.1190.0950.0640.0640.4830.0620.0960.1950.0950.0620.0950.3360.0640.0640.0950.0660.0640.0620.5930.0660.4160.0640.0640.0620.0660.0660.0640.1800.3360.0950.3360.3360.0620.2930.0640.3360.0640.0250.0660.0640.0620.0620.0950.0660.0930.0620.3410.3550.0950.0640.0620.0660.0640.3360.3480.0620.0640.0640.1430.2930.0620.0620.0640.0620.0640.0640.0220.209
cold_hands_and_feets0.0760.6770.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.9700.0130.0260.0590.0641.0000.0140.0140.0270.0130.0270.0510.0130.0520.0120.6860.0510.0120.0130.5340.0130.9700.0450.0120.0270.0280.1560.0130.0100.0810.0930.0130.0130.0140.0270.0130.0130.0130.4690.0130.0580.0580.0130.0130.4510.0820.0370.0140.0590.0380.6570.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3110.0130.9420.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.9700.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.9420.0450.0130.0130.0650.070
coma0.3050.0290.0280.9700.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.0460.0150.0140.0270.0610.0660.0141.0000.0150.0280.0140.0280.0530.0140.4100.0130.0290.0530.0130.0140.0380.0140.0150.0470.0130.0280.0290.1790.0140.0120.0840.2360.0140.0140.0150.0280.0140.0140.0140.0470.0140.0600.3680.0140.0140.0460.2840.0380.0150.0610.0390.0280.0140.0140.0470.0130.0290.2660.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.0390.0150.3190.0140.0140.0130.0150.0150.0140.0290.0150.0280.0150.0150.0130.0130.0140.0150.0140.0660.0150.0140.0130.0130.0280.9960.0280.0130.0600.0390.0280.0140.0130.0150.0140.0150.0140.0130.0140.0140.1800.0130.0130.0130.0140.0460.0140.0140.3530.309
congestion0.0790.0290.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.0460.0150.0140.0270.3870.3360.0140.0151.0000.0280.0140.6860.4120.0140.0530.0130.0290.0530.0130.0140.0380.0140.0150.0470.0130.0280.0290.1790.0140.0120.2680.2360.0140.0140.0150.0280.0140.0140.0140.0470.0140.0600.0600.0140.0140.0460.0850.0380.9960.3630.0390.0280.0140.0140.4820.0130.0290.0840.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.5650.0150.3470.0140.0140.0130.0150.0150.0140.0290.9960.0280.9960.0150.0130.0130.0140.9960.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.0600.5400.0280.0140.0130.0150.0140.9960.0140.0130.0140.0140.1240.0130.0130.0130.0140.0460.0140.0140.0670.072
constipation0.1410.0450.0430.0270.0270.0270.0440.6570.0260.0270.0270.0280.6570.0570.0670.0280.0270.0430.0870.2000.0270.0280.0281.0000.0270.0430.0760.0270.0770.0260.0450.2470.0260.0270.0560.0270.0280.0680.0260.0440.0450.0450.0270.0250.1250.1080.0270.0270.0280.0430.0270.0270.0270.0690.6570.0860.0860.0270.0270.0670.1200.0570.0280.0870.0580.0440.0270.0270.0690.0260.0450.1240.0440.0260.0440.0280.6570.6570.0440.0280.0270.0260.0580.0280.3040.0270.0270.0260.0280.0280.0270.0450.0280.0440.0280.0280.0260.0260.0270.0280.0270.0940.0280.0270.0260.0260.0440.0280.0430.0260.0860.0570.0440.0270.0260.0280.0270.0280.6570.0260.0270.0270.0340.0260.0260.0260.0270.0670.0270.0270.0960.103
continuous_feel_of_urine0.0760.0280.0270.0130.0130.0130.0270.0130.0120.9420.0130.0140.0130.0370.0440.0140.0130.6360.0590.0640.0130.0140.0140.0271.0000.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.8830.0810.0930.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
continuous_sneezing0.1100.0450.0430.0270.0270.0270.0430.0270.0260.0270.0270.0280.0270.0560.0660.0280.0270.0420.2300.4450.0270.0280.6860.0430.0271.0000.2520.0270.0760.0260.0440.0750.0260.0270.0550.0270.0280.0670.0260.0430.0440.0380.0270.0240.1300.1000.0270.0270.0280.0430.0270.0270.0270.0680.0270.0840.0850.0270.0270.0660.1180.0560.6860.2120.0570.0430.0270.0270.3050.0260.0440.1180.0430.0260.0430.0280.0270.0270.0430.0280.0270.0260.3690.0280.3730.0270.0270.0260.0280.0280.0270.0440.6860.0430.6860.0280.0260.6060.0270.6860.0270.0920.0280.0270.0260.0260.0430.0280.0430.0260.0840.3500.0430.0270.0260.0280.0270.6860.0270.0260.0270.0270.1720.6060.0260.0260.0270.0660.0270.0270.0950.101
cough0.1840.0790.2330.0510.0510.0510.0760.0510.0500.0510.0510.4120.0510.0960.5880.0530.0510.0740.6770.4010.0510.0530.4120.0760.0510.2521.0000.0510.1280.0500.0780.1280.0500.0510.0950.0510.0530.1140.0500.2280.2590.2580.0510.0480.0290.3840.0510.0510.0530.0750.0510.0510.0510.1160.0510.1420.1430.0510.0510.1130.0220.0960.4120.4540.1790.0760.0510.3750.1270.0500.0780.1970.0760.0500.0760.0530.0510.0510.0760.0530.0510.0500.7280.0530.3760.0510.0510.0500.0530.0530.0510.0780.4120.0760.4120.4120.0500.0500.0510.4120.0510.1550.0530.0510.0500.0500.0760.0530.2520.0500.2550.4520.0760.0510.0500.0530.0510.4120.0510.3880.0510.0510.0000.0500.0500.0500.0510.1210.0510.0510.0310.170
cramps0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.9420.0260.0590.0640.0130.0140.0140.0270.0130.0270.0511.0000.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1730.0130.0100.0810.0930.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.6570.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.9420.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.9130.0140.9420.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
dark_urine0.6640.0790.0760.3980.0520.0520.0770.0520.0500.0520.0520.0530.0520.0970.1130.0530.0520.0750.1450.1580.0520.4100.0530.0770.0520.0760.1280.0521.0000.0500.0780.0830.0500.0520.0960.0520.0530.1150.0500.0770.0780.2700.0520.0480.1970.0810.0520.0520.0530.0760.0520.0520.0520.1160.0520.2520.4480.0520.0520.1190.4560.0970.0530.0570.1780.0770.0520.0520.1250.0500.0780.2860.0770.0500.0770.0530.0520.0520.0770.0530.0520.0500.0990.0530.4130.0520.0520.0500.4100.4100.0520.0780.0530.0770.0530.0530.0500.0500.0520.0530.0520.1560.0530.0520.0500.0500.0770.4100.0760.0500.1430.0980.0770.0520.0500.0530.0520.0530.0520.0500.0520.0520.2730.0500.0500.0500.0520.1190.0520.3980.6060.709
dehydration0.0740.0270.0260.0120.0120.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0501.0000.0270.3880.0110.0120.0350.0120.0130.0440.0110.0260.0270.1180.0120.0090.0790.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.0260.0110.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.3280.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.0110.0120.0130.0120.0620.0130.0120.0110.0110.0260.0130.0260.8820.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1520.0110.0110.0110.0120.0430.0120.0120.0630.068
depression0.1130.4790.4460.0280.0280.0280.0450.0280.0270.0280.0280.0290.0280.3420.0680.7040.0280.0430.0880.0960.6860.0290.0290.0450.0280.0440.0780.0280.0780.0271.0000.0780.0270.0280.3690.0280.7040.2800.0270.0450.0460.0270.0280.0250.1210.1360.0280.0280.0290.4460.0280.0280.0280.6630.0280.0870.0870.0280.0280.3020.1220.0570.0290.0890.0590.4660.0280.0280.0700.0270.0460.1210.0450.0270.0450.0290.0280.0280.0450.0290.0280.0270.0590.0290.2930.0280.6860.0270.0290.0290.0280.0460.0290.0450.0290.0290.0270.0270.0280.0290.0280.0950.0290.0280.0270.0270.4390.0290.0440.0270.0870.0580.0450.0280.0270.7040.0280.0290.0280.0270.0280.6480.1770.0270.0270.0270.6860.0680.0280.0280.0970.104
diarrhoea0.1520.2540.0750.0510.0510.0510.0760.4000.0500.0510.0510.0530.0510.0960.1120.0530.0510.0740.1440.2140.0510.0530.0530.2470.0510.0750.1280.0510.0830.3880.0781.0000.0500.0510.0950.0510.0530.1180.0500.0760.2410.0000.0510.0480.1200.0830.0510.0510.0530.0750.0510.0510.0510.1270.0510.1420.0520.0510.0510.1130.0330.0960.0530.1450.1790.2470.0510.0510.3610.0500.2590.2810.0760.0500.0760.0530.0510.0510.0760.0530.0510.0500.0980.0530.2810.0510.0510.0500.0530.0530.0510.0780.0530.2470.0530.0530.0500.0500.0510.0530.0510.1550.0530.0510.0500.0500.0760.0530.0750.3880.2440.0970.0760.0510.0500.0530.0510.0530.4000.0500.0510.0510.2610.0500.0500.0500.0510.1210.0510.0510.0310.000
dischromic _patches0.0740.0270.0260.0120.0120.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0500.0110.0270.0501.0000.0120.0350.0120.0130.0440.0110.0260.0270.1180.0120.0090.0790.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.3240.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.0260.8820.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.3280.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.0110.0120.0130.0120.2960.0130.0120.0110.0110.0260.0130.0260.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1170.0110.0110.0110.0120.0430.0120.0120.0630.068
distention_of_abdomen0.2770.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0121.0000.0360.0130.0140.0450.0120.0270.0280.1220.9420.0100.0810.0930.0130.9420.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.4670.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.9420.0120.0140.0130.0140.0130.0120.0130.0130.1740.0120.0120.0120.0130.0450.0130.0130.0650.300
dizziness0.1380.3630.0550.0360.0360.0360.3290.0360.0350.0360.0360.0380.0360.0710.0830.5500.0360.0540.1100.1170.5340.0380.0380.0560.0360.0550.0950.0360.0960.0350.3690.0950.0350.0361.0000.0360.5500.0850.0350.0560.0570.0460.0360.0340.0310.1660.0360.0360.0380.0550.0360.0360.0360.2210.0360.1060.1070.0360.5020.2120.1480.5900.0380.1080.0720.3520.0360.0360.0860.0350.0570.1480.3520.0350.0560.0380.0360.0360.0560.0380.0360.0350.0720.0380.3730.0360.5340.0350.0380.0380.0360.0570.0380.0560.0380.0380.0350.0350.0360.0380.0360.1160.0380.0360.0350.0350.0560.0380.0550.0350.1060.0720.0560.0360.0350.5500.0360.0380.0360.0350.0360.0360.2150.0350.5170.0350.5340.0840.0360.0360.1190.127
drying_and_tingling_lips0.0760.0280.0270.0130.0130.9420.0270.0130.0120.0130.0130.0140.0130.5290.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0361.0000.0140.4760.0120.0270.0280.1730.0130.0100.2600.0930.0130.0130.0140.0270.0130.0130.0130.4690.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.2580.0270.0120.0270.0140.0130.0130.0270.9700.0130.0120.0380.0140.3110.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.9700.0130.0120.0120.0270.0140.0270.0120.3600.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1740.0120.0120.0120.0130.0450.0130.0130.0650.070
enlarged_thyroid0.0790.6950.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.0460.9960.0140.0270.0610.0660.9700.0150.0150.0280.0140.0280.0530.0140.0530.0130.7040.0530.0130.0140.5500.0141.0000.0470.0130.0280.0290.1620.0140.0120.0840.0950.0140.0140.0150.0280.0140.0140.0140.4820.0140.0600.0600.0140.0140.4650.0850.0380.0150.0610.0390.6760.0140.0140.0470.0130.0290.0840.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.0390.0150.3190.0140.9700.0130.0150.0150.0140.0290.0150.0280.0150.0150.0130.0130.0140.0150.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.0600.0390.0280.0140.0130.9960.0140.0150.0140.0130.0140.0140.1240.0130.0130.0130.9700.0460.0140.0140.0670.072
excessive_hunger0.1640.2940.2910.0450.0450.4760.0680.0450.0440.0450.0450.0470.0450.8150.1000.0470.0450.0660.1290.1400.0450.0470.0470.0680.0450.0670.1140.0450.1150.0440.2800.1180.0440.0450.0850.4760.0471.0000.0440.0680.2800.2110.0450.0420.1900.1980.0450.0450.4620.2910.0450.0450.4480.7010.0450.1270.1280.0450.0450.1550.1770.0850.0470.1300.0870.2850.0450.0450.1030.0440.2990.0000.0680.0440.2850.0470.0450.0450.0680.4890.0450.0440.0870.4620.3250.0450.0450.0440.0470.0470.0450.0690.0470.6430.0470.0470.0440.0440.0450.0470.0450.1390.4890.0450.0440.0440.2850.0470.0670.0440.3190.0860.0680.0450.0440.0470.0450.0470.0450.0440.0450.4480.0920.0440.0440.0440.0450.4150.0450.0450.1420.152
extra_marital_contacts0.0740.0270.0260.0120.0120.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0500.0110.0270.0500.0110.0120.0350.0120.0130.0441.0000.0260.0270.1180.0120.0090.0790.2220.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.8820.0270.0800.0260.0110.0260.0130.0120.0120.0260.0130.0120.8820.0360.0130.4540.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.0110.0120.0130.0120.0620.0130.0120.0110.0110.0260.0130.0260.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1170.0110.0110.0110.0120.0430.0120.0120.0630.068
family_history0.1110.0450.0430.0270.0270.0270.0440.0270.0260.0270.0270.0280.0270.0570.2900.0280.0270.0430.0870.0950.0270.0280.0280.0440.0270.0430.2280.0270.0770.0260.0450.0760.0260.0270.0560.0270.0280.0680.0261.0000.0450.2370.0270.0250.1190.0950.0270.0270.0280.0430.0270.0270.0270.0690.0270.0860.0860.0270.0270.0670.1230.0570.0280.0870.0580.0440.0270.6570.0690.0260.0450.1240.0440.0260.0440.0280.0270.0270.0440.0280.0270.0260.0580.0280.2890.0270.0270.0260.0280.0280.0270.0450.0280.0440.0280.0280.0260.0260.0270.0280.0270.0940.0280.0270.0260.0260.0440.0280.0430.0260.0860.0570.0440.0270.0260.0280.0270.0280.0270.0260.0270.0270.1740.0260.0260.0260.0270.0670.0270.0270.1650.162
fast_heart_rate0.1130.4520.0440.0280.0280.0280.0450.0280.0270.0280.0280.0290.0280.0570.3050.0290.0280.0430.2360.1950.0280.0290.0290.0450.0280.0440.2590.0280.0780.0270.0460.2410.0270.0280.0570.0280.0290.2800.0270.0451.0000.2530.0280.0250.1200.1030.0280.0280.0290.0440.0280.0280.0280.2940.0280.0870.0870.0280.0280.0680.1220.0570.0290.2180.0590.4390.0280.0280.0700.0270.4590.1210.0450.0270.0450.0290.0280.0280.0450.0290.0280.0270.3570.0290.2940.0280.0280.0270.0290.0290.0280.0460.0290.4390.0290.7040.0270.0270.0280.0290.0280.0950.0290.0280.0270.0270.0450.0290.0440.0270.5240.0580.0450.0280.0270.0290.0280.0290.0280.0270.0280.0280.1770.0270.0270.0270.0280.2820.0280.0280.0970.104
fatigue0.1490.2450.1730.1730.1220.1730.0450.1900.1180.1220.1220.1790.1220.1320.2030.1620.1730.1710.0800.2690.1560.1790.1790.0450.1220.0380.2580.1730.2700.1180.0270.0000.1180.1220.0460.1730.1620.2110.1180.2370.2531.0000.1220.1150.1040.4120.1220.1220.1790.1730.1220.1220.1730.2100.1220.0680.0640.1220.1220.3530.3150.2190.1790.4640.1320.2370.1220.1560.0480.1180.0400.2060.1760.1180.2490.1790.1220.1220.1760.1790.1220.1180.3160.1790.5980.1730.1560.1180.1790.1790.1220.2530.1790.2490.1790.1790.1180.1180.1220.1790.1220.1030.1790.1220.1180.1180.1760.1790.1730.1180.1990.3130.1760.1220.1670.1620.1730.1790.1900.1180.1220.1220.0000.1180.1180.1180.1560.3620.1220.1730.2580.194
fluid_overload.10.2770.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.9420.0360.0130.0140.0450.0120.0270.0280.1221.0000.0100.0810.0930.0130.9420.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.4670.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.9420.0120.0140.0130.0140.0130.0120.0130.0130.1740.0120.0120.0120.0130.0450.0130.0130.0650.300
foul_smell_of urine0.0720.0260.0240.0100.0100.0100.0250.0100.0090.8830.0100.0120.0100.0340.0410.0120.0100.5920.0550.0600.0100.0120.0120.0250.8830.0240.0480.0100.0480.0090.0250.0480.0090.0100.0340.0100.0120.0420.0090.0250.0250.1150.0101.0000.0770.0870.0100.0100.0120.0240.0100.0100.0100.0430.0100.0540.0550.0100.0100.0420.0770.0340.0120.0560.0350.0250.0100.0100.0430.0090.0250.0770.0250.0090.0250.0120.0100.0100.0250.0120.0100.0090.0350.0120.3190.0100.0100.0090.0120.0120.0100.0250.0120.0250.0120.0120.0090.0090.0100.0120.0100.0600.0120.0100.0090.0090.0250.0120.0240.0090.0540.0350.0250.0100.0090.0120.0100.0120.0100.0090.0100.0100.1140.0090.0090.0090.0100.0420.0100.0100.0610.066
headache0.1530.1220.1300.0810.2410.2600.1530.2600.0790.0810.0810.0840.0810.2590.1720.0840.0810.1150.0660.3400.0810.0840.2680.1250.0810.1300.0290.0810.1970.0790.1210.1200.0790.0810.0310.2600.0840.1900.0790.1190.1200.1040.0810.0771.0000.2560.0810.0810.0840.1300.0810.0810.0810.1830.0810.0650.0580.0810.2410.0000.0400.2480.2680.2390.0460.1190.0810.0810.3800.0790.1200.3280.1190.0790.1190.2870.0810.0810.1190.2680.0810.0790.0580.0840.4680.0810.0810.0790.0840.0840.0810.3930.2680.1190.2680.0840.0790.0790.0810.2680.0810.0510.2680.0810.2520.0790.1250.0840.1170.0790.0820.2540.1190.0810.0790.0840.0810.2680.2600.0790.2600.2600.1980.0790.0790.2320.0810.1740.0810.0810.2430.260
high_fever0.0540.1380.1330.2290.0930.0930.1080.2470.0900.0930.1930.2360.0930.1680.3130.0950.0930.1310.1930.5400.0930.2360.2360.1080.0930.1000.3840.0930.0810.0900.1360.0830.0900.0930.1660.0930.0950.1980.2220.0950.1030.4120.0930.0870.2561.0000.0930.0930.0950.1330.0930.0930.0930.2010.0930.0340.0310.0930.0930.0240.1390.1680.2360.4640.2170.1350.0930.2290.3140.2220.1360.1350.1350.0900.1350.2360.0930.0930.1350.0950.0930.2220.4170.0950.4320.0930.0930.0900.0950.0950.1930.3340.2360.1350.2360.2360.0900.0900.0930.2360.0930.1160.0950.0930.0900.0900.1350.2360.1330.0900.1780.4130.1350.0930.0900.0950.0930.2360.2470.0900.0930.0930.1140.0900.0900.0900.0930.1390.1930.0930.0000.040
hip_joint_pain0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.0931.0000.0130.0140.0270.0130.0130.0130.0460.0130.0580.3580.9420.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.6570.0120.0270.0140.0130.0130.6570.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.6570.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
history_of_alcohol_consumption0.2770.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.9420.0360.0130.0140.0450.0120.0270.0280.1220.9420.0100.0810.0930.0131.0000.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.4670.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.9420.0120.0140.0130.0140.0130.0120.0130.0130.1740.0120.0120.0120.0130.0450.0130.0130.0650.300
increased_appetite0.0790.0290.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.5450.0460.0150.0140.0270.0610.0660.0140.0150.0150.0280.0140.0280.0530.0140.0530.0130.0290.0530.0130.0140.0380.0140.0150.4620.0130.0280.0290.1790.0140.0120.0840.0950.0140.0141.0000.0280.0140.0140.9700.0470.0140.0600.0600.0140.0140.4650.0850.0380.0150.0610.0390.0280.0140.0140.0470.0130.0290.0840.0280.0130.6760.0150.0140.0140.0280.0150.0140.0130.0390.9960.3470.0140.0140.0130.0150.0150.0140.0290.0150.6760.0150.0150.0130.0130.0140.0150.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.0600.0390.0280.0140.0130.0150.0140.0150.0140.0130.0140.0140.1240.0130.0130.0130.0140.4650.0140.0140.0670.072
indigestion0.1310.0450.4600.0270.0270.0270.0430.0270.0260.0270.0270.0280.0270.3540.0660.0280.0270.0420.0860.0930.0270.0280.0280.0430.0270.0430.0750.0270.0760.0260.4460.0750.0260.0270.0550.0270.0280.2910.0260.0430.0440.1730.0270.0240.1300.1330.0270.0270.0281.0000.0270.6270.0270.2850.0270.0840.0850.0270.0270.0660.1000.0560.0280.0860.0570.0430.0270.0270.0680.0260.0440.1180.0430.0260.0430.0280.0270.0270.0430.0280.6270.0260.0570.0280.4780.0270.0270.0260.0280.0280.0270.0440.0280.0430.0280.0280.0260.0260.0270.0280.0270.0920.0280.0270.0260.0260.4530.0280.0430.0260.0840.0560.0430.0270.0260.0280.0270.0280.0270.0260.0270.6660.0270.0260.0260.0260.0270.0660.0270.0270.0950.101
inflammatory_nails0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.0930.0130.0130.0140.0271.0000.0130.0130.0460.0130.0580.3580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.9420.0140.9420.3290.0140.9420.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
internal_itching0.2770.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.0930.0130.0130.0140.6270.0131.0000.0130.0460.0130.0580.0580.0130.0130.0450.2380.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.9420.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1740.0120.0120.0120.0130.0450.0130.0130.0650.070
irregular_sugar_level0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.5290.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.4480.0120.0270.0280.1730.0130.0100.0810.0930.0130.0130.9700.0270.0130.0131.0000.0460.0130.0580.0580.0130.0130.4510.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.6570.0140.0130.0130.0270.0140.0130.0120.0380.9700.3380.0130.0130.0120.0140.0140.0130.0280.0140.6570.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.4510.0130.0130.0650.070
irritability0.1670.6920.2850.0460.0460.4690.0690.0460.0440.0460.0460.0470.0460.5110.1010.4820.0460.0670.1310.1420.4690.0470.0470.0690.0460.0680.1160.0460.1160.0440.6630.1270.0440.0460.2210.4690.4820.7010.0440.0690.2940.2100.0460.0430.1830.2010.0460.0460.0470.2850.0460.0460.0461.0000.0460.1290.1290.0460.0460.1650.1790.0870.0470.1310.0880.6730.0460.0460.1040.0440.3140.0000.0690.0440.0690.0470.0460.0460.0690.4820.0460.0440.0880.0470.4930.0460.4690.0440.0470.0470.0460.0700.0470.3000.0470.0470.0440.0440.0460.0470.0460.1410.4820.0460.0440.0440.2800.0470.0680.0440.3240.0880.0690.0460.0440.4820.0460.0470.0460.0440.0460.4420.0980.0440.0440.0440.4690.1650.0460.0460.1440.154
irritation_in_anus0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.9420.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.6570.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.0930.0130.0130.0140.0270.0130.0130.0130.0461.0000.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.9420.9420.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
itching0.2620.0880.0840.0580.0580.0580.0860.0580.0560.0580.0580.0600.0580.1070.1250.0600.0580.2060.1610.1750.0580.0600.0600.0860.0580.0840.1420.0580.2520.0560.0870.1420.3240.0580.1060.0580.0600.1270.0560.0860.0870.0680.0580.0540.0650.0340.0580.0580.0600.0840.0580.0580.0580.1290.0581.0000.1590.0580.0580.3100.2290.1070.0600.2200.1330.0860.0580.0580.1290.0560.0870.0670.0860.3240.0860.0600.0580.0580.0860.0600.0580.0560.1090.0600.3080.0580.0580.0560.3700.3700.0580.2250.0600.0860.0600.0600.0560.0560.0580.0600.0580.3180.0600.0580.0560.3480.0860.0600.2010.0560.1580.1360.0860.0580.0560.0600.0580.0600.0580.0560.0580.0580.0550.0560.0560.0560.0580.0900.0580.3600.1720.300
joint_pain0.2680.0890.0850.3580.0580.0580.2280.0580.0560.0580.0580.0600.0580.1080.1260.0600.0580.0830.1620.0000.0580.3680.0600.0860.0580.0850.1430.0580.4480.0560.0870.0520.0560.0580.1070.0580.0600.1280.0560.0860.0870.0640.0580.0550.0580.0310.3580.0580.0600.0850.3580.0580.0580.1290.0580.1591.0000.3580.0580.1270.3930.1080.0600.0090.1450.0860.0580.0580.3100.0560.0870.3860.2110.0560.0860.3680.0580.0580.2110.0600.0580.0560.1100.0600.2950.0580.0580.0560.0600.0600.0580.2060.0600.0860.0600.0600.0560.0560.3580.0600.3580.1700.0600.3580.0560.0560.0860.3680.0850.0560.1590.1090.2110.0580.0560.0600.0580.0600.0580.0560.0580.0580.1990.0560.0560.0560.0580.1270.0580.0580.3500.297
knee_pain0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.0930.9420.0130.0140.0270.0130.0130.0130.0460.0130.0580.3581.0000.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.6570.0120.0270.0140.0130.0130.6570.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.6570.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
lack_of_concentration0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.3310.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.5020.0130.0140.0450.0120.0270.0280.1220.0130.0100.2410.0930.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0131.0000.0450.0820.5290.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3110.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
lethargy0.0150.2970.0660.0450.0450.0450.0670.0450.0430.0450.0450.0460.0450.2080.0990.4650.0450.0650.1280.1390.4510.0460.0460.0670.0450.0660.1130.0450.1190.0430.3020.1130.0430.0450.2120.0450.4650.1550.0430.0670.0680.3530.0450.0420.0000.0240.0450.0450.4650.0660.0450.0450.4510.1650.0450.3100.1270.0450.0451.0000.1790.0850.0460.3250.2020.2880.0450.0450.1020.0430.0680.1750.0670.0430.2880.0460.0450.0450.0670.0460.0450.0430.0860.4650.3200.0450.4510.0430.4650.4650.0450.3020.0460.2880.0460.0460.0430.0430.0450.0460.0450.0650.0460.0450.0430.0430.0670.0460.0660.0430.1260.2050.0670.0450.0430.4650.0450.0460.0450.0430.0450.0450.2540.0430.0430.0430.4510.1570.0450.4510.0700.039
loss_of_appetite0.4850.1230.1180.2770.0820.0820.1500.0820.0800.0820.0820.2840.0820.1500.0000.0850.0820.1160.0570.0510.0820.2840.0850.1200.0820.1180.0220.0820.4560.0800.1220.0330.0800.0820.1480.0820.0850.1770.0800.1230.1220.3150.0820.0770.0400.1390.0820.0820.0850.1000.0820.2380.0820.1790.0820.2290.3930.0820.0820.1791.0000.1500.0850.4080.4800.1200.0820.0820.1890.0800.1220.4510.1200.0800.1200.2840.0820.0820.1200.0850.2380.0800.0670.0850.4450.0820.0820.0800.2650.2650.0820.3890.0850.1200.0850.0850.0800.0800.0820.0850.0820.0470.0850.0820.0800.0800.1200.2840.1180.0800.0600.2730.1200.0820.0800.0850.0820.0850.0820.0800.0820.0820.3140.0800.0800.0800.0820.0000.0820.2570.7670.543
loss_of_balance0.1390.0580.0560.0370.0370.0370.3250.0370.0350.0370.0370.0380.0370.0720.0840.0380.0370.0550.1210.1180.0370.0380.0380.0570.0370.0560.0960.0370.0970.0350.0570.0960.0350.0370.5900.0370.0380.0850.0350.0570.0570.2190.0370.0340.2480.1680.0370.0370.0380.0560.0370.0370.0370.0870.0370.1070.1080.0370.5290.0850.1501.0000.0380.1090.0730.0570.0370.0370.0870.0350.0570.0510.3480.0350.0570.0380.0370.0370.0570.0380.0370.0350.0730.0380.5960.0370.0370.0350.0380.0380.0370.0570.0380.0570.0380.0380.0350.0350.0370.0380.0370.1170.0380.0370.5130.0350.0570.0380.0560.0350.1070.0720.0570.0370.0350.0380.0370.0380.0370.0350.5290.0370.0380.0350.5130.0350.0370.0850.0370.0370.1200.129
loss_of_smell0.0790.0290.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.0460.0150.0140.0270.3870.3360.0140.0150.9960.0280.0140.6860.4120.0140.0530.0130.0290.0530.0130.0140.0380.0140.0150.0470.0130.0280.0290.1790.0140.0120.2680.2360.0140.0140.0150.0280.0140.0140.0140.0470.0140.0600.0600.0140.0140.0460.0850.0381.0000.3630.0390.0280.0140.0140.4820.0130.0290.0840.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.5650.0150.3470.0140.0140.0130.0150.0150.0140.0290.9960.0280.9960.0150.0130.0130.0140.9960.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.0600.5400.0280.0140.0130.0150.0140.9960.0140.0130.0140.0140.1240.0130.0130.0130.0140.0460.0140.0140.0670.072
malaise0.0450.0900.0860.0590.0590.0590.2230.0590.0570.0590.0590.3850.0590.1090.3170.0610.0590.0850.4140.5100.0590.0610.3630.0870.0590.2120.4540.0590.0570.0570.0890.1450.0570.0590.1080.0590.0610.1300.0570.0870.2180.4640.0590.0560.2390.4640.0590.0590.0610.0860.0590.0590.0590.1310.0590.2200.0090.0590.0590.3250.4080.1090.3631.0000.4110.0870.0590.0590.3030.0570.0890.0660.0870.0570.0870.3630.0590.0590.0870.0610.0590.0570.6540.0610.4530.0590.0590.0570.3850.3850.0590.5300.3630.0870.3630.3630.0570.0570.0590.3630.0590.1730.0610.0590.0570.0570.0870.0610.0860.0570.2100.6610.0870.0590.0570.0610.0590.3630.0590.0570.0590.0590.0590.0570.0570.0570.0590.0960.0590.3750.1920.019
mild_fever0.0740.0600.0570.0380.0380.0380.0580.0380.0360.0380.0380.5650.0380.0730.2210.0390.0380.0560.1560.1190.0380.0390.0390.0580.0380.0570.1790.0380.1780.0360.0590.1790.0360.0380.0720.0380.0390.0870.0360.0580.0590.1320.0380.0350.0460.2170.0380.0380.0390.0570.0380.0380.0380.0880.0380.1330.1450.0380.0380.2020.4800.0730.0390.4111.0000.0580.0380.0380.2270.0360.0590.0560.0580.0360.0580.0390.0380.0380.0580.0390.0380.0360.2860.0390.4210.0380.0380.0360.0390.0390.0380.3570.0390.0580.0390.0390.0360.0360.0380.0390.0380.1090.0390.0380.0360.0360.0580.0390.0570.0360.1470.6210.0580.0380.0360.0390.0380.0390.0380.0360.0380.0380.1440.0360.0360.0360.0380.2180.0380.0380.3820.096
mood_swings0.1110.9710.0430.0270.0270.0270.0440.0270.0260.0270.0270.0280.0270.0570.0670.6760.0270.0430.0870.0950.6570.0280.0280.0440.0270.0430.0760.0270.0770.0260.4660.2470.0260.0270.3520.0270.6760.2850.0260.0440.4390.2370.0270.0250.1190.1350.0270.0270.0280.0430.0270.0270.0270.6730.0270.0860.0860.0270.0270.2880.1200.0570.0280.0870.0581.0000.0270.0270.0690.0260.4660.1190.0440.0260.0440.0280.0270.0270.0440.0280.0270.0260.0580.0280.4460.0270.6570.0260.0280.0280.0270.0450.0280.4460.0280.0280.0260.0260.0270.0280.0270.0940.0280.0270.0260.0260.0440.0280.0430.0260.2130.0570.0440.0270.0260.6760.0270.0280.0270.0260.0270.0270.1740.0260.0260.0260.6570.2880.0270.0270.0960.103
movement_stiffness0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.0930.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0271.0000.0130.0460.0120.6480.0820.0270.0120.0270.0140.0130.0130.6570.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.6570.0140.0270.0120.0580.0370.6570.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
mucoid_sputum0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.4550.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.3750.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.6570.0280.1560.0130.0100.0810.2290.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0131.0000.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
muscle_pain0.0190.0710.0680.0460.0460.0460.3000.0460.0440.0460.0460.0470.0460.0870.1010.0470.0460.0670.1030.4830.0460.0470.4820.0690.0460.3050.1270.0460.1250.0440.0700.3610.0440.0460.0860.0460.0470.1030.0440.0690.0700.0480.0460.0430.3800.3140.0460.0460.0470.0680.0460.0460.0460.1040.0460.1290.3100.0460.0460.1020.1890.0870.4820.3030.2270.0690.0460.0461.0000.0440.0700.3760.0690.0440.0690.4550.0460.0460.0690.0470.0460.0440.2270.0470.3530.0460.0460.0440.0470.0470.0460.2750.4820.0690.4820.0470.0440.0440.0460.4820.0460.0580.0470.0460.0440.0440.0690.0470.0680.0440.0950.2140.0690.0460.0440.0470.0460.4820.0460.0440.0460.0460.2130.0440.0440.0440.0460.1020.0460.0460.0740.043
muscle_wasting0.0740.0270.0260.0120.0120.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0500.0110.0270.0500.0110.0120.0350.0120.0130.0440.8820.0260.0270.1180.0120.0090.0790.2220.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0441.0000.0270.0800.0260.0110.0260.0130.0120.0120.0260.0130.0120.8820.0360.0130.4540.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.0110.0120.0130.0120.0620.0130.0120.0110.0110.0260.0130.0260.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1170.0110.0110.0110.0120.0430.0120.0120.0630.068
muscle_weakness0.1130.4790.0440.0280.0280.0280.0450.0280.0270.0280.0280.0290.0280.0570.0680.0290.0280.0430.0880.0960.0280.0290.0290.0450.0280.0440.0780.0280.0780.0270.0460.2590.0270.0280.0570.0280.0290.2990.0270.0450.4590.0400.0280.0250.1200.1360.0280.0280.0290.0440.0280.0280.0280.3140.0280.0870.0870.0280.0280.0680.1220.0570.0290.0890.0590.4660.6480.0280.0700.0271.0000.1210.0450.0270.0450.0290.0280.0280.4390.0290.0280.0270.0590.0290.2930.0280.0280.0270.0290.0290.0280.0460.0290.4660.0290.0290.0270.0270.0280.0290.0280.0950.0290.0280.0270.0270.4390.0290.0440.0270.2250.0580.4390.0280.0270.0290.0280.0290.0280.0270.0280.0280.1770.0270.0270.0270.0280.3020.0280.0280.0970.104
nausea0.3600.1230.1180.2580.0820.2580.1510.2580.0800.0820.0820.0840.0820.0510.1730.0840.0820.1160.2230.1950.0820.2660.0840.1240.0820.1180.1970.0820.2860.0800.1210.2810.0800.0820.1480.2580.0840.0000.0800.1240.1210.2060.0820.0770.3280.1350.0820.0820.0840.1180.0820.0820.0820.0000.0820.0670.3860.0820.0820.1750.4510.0510.0840.0660.0560.1190.0820.0820.3760.0800.1211.0000.1190.0800.1190.2850.0820.0820.1190.2660.0820.0800.1520.0840.4760.0820.0820.0800.0840.0840.0820.1330.0840.1190.0840.0840.0800.0800.0820.0840.0820.0890.2660.0820.2500.0800.1190.2660.1180.0800.0790.1500.1190.0820.0800.0840.0820.0840.2580.0800.2580.0820.5240.0800.0800.0800.0820.1750.0820.0820.4590.405
neck_pain0.1110.0450.0430.0270.0270.0270.4180.0270.0260.0270.0270.0280.0270.0570.0670.0280.0270.0430.0870.0950.0270.0280.0280.0440.0270.0430.0760.0270.0770.0260.0450.0760.0260.0270.3520.0270.0280.0680.0260.0440.0450.1760.0270.0250.1190.1350.6570.0270.0280.0430.0270.0270.0270.0690.0270.0860.2110.6570.0270.0670.1200.3480.0280.0870.0580.0440.0270.0270.0690.0260.0450.1191.0000.0260.0440.0280.0270.0270.4460.0280.0270.0260.0580.0280.3040.0270.0270.0260.0280.0280.0270.0450.0280.0440.0280.0280.0260.0260.0270.0280.0270.0940.0280.0270.0260.0260.0440.0280.0430.0260.0860.0570.4460.0270.0260.0280.0270.0280.0270.0260.0270.0270.1740.0260.6370.0260.0270.0670.0270.0270.0960.103
nodal_skin_eruptions0.0740.0270.0260.0120.0120.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0500.0110.0270.0500.8820.0120.0350.0120.0130.0440.0110.0260.0270.1180.0120.0090.0790.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.3240.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.0261.0000.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.3280.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.0110.0120.0130.0120.2960.0130.0120.0110.0110.0260.0130.0260.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1170.0110.0110.0110.0120.0430.0120.0120.0630.068
obesity0.1110.0450.0430.0270.0270.0270.0440.0270.0260.0270.0270.0280.0270.3480.0670.0280.6570.0430.0870.0950.0270.0280.0280.0440.0270.0430.0760.6570.0770.0260.0450.0760.0260.0270.0560.0270.0280.2850.0260.0440.0450.2490.0270.0250.1190.1350.0270.0270.6760.0430.0270.0270.6570.0690.0270.0860.0860.0270.0270.2880.1200.0570.0280.0870.0580.0440.0270.0270.0690.0260.0450.1190.0440.0261.0000.0280.0270.0270.0440.0280.0270.0260.0580.6760.3040.6570.0270.0260.0280.0280.0270.0450.0280.4460.0280.0280.0260.0260.0270.0280.0270.0940.0280.0270.0260.0260.0440.0280.0430.0260.0860.0570.0440.0270.6370.0280.6570.0280.0270.0260.0270.0270.1740.0260.0260.0260.0270.2880.0270.0270.0960.103
pain_behind_the_eyes0.0790.0290.0280.0140.0140.0140.7140.0140.0130.0140.0140.0150.0140.0380.0460.0150.0140.0270.0610.3360.0140.0150.0150.0280.0140.0280.0530.0140.0530.0130.0290.0530.0130.0140.0380.0140.0150.0470.0130.0280.0290.1790.0140.0120.2870.2360.0140.0140.0150.0280.0140.0140.0140.0470.0140.0600.3680.0140.0140.0460.2840.0380.0150.3630.0390.0280.0140.0140.4550.0130.0290.2850.0280.0130.0281.0000.0140.0140.0280.0150.0140.0130.0390.0150.3470.0140.0140.0130.0150.0150.0140.6670.0150.0280.0150.0150.0130.0130.0140.0150.0140.3390.0150.0140.0130.0130.0280.0150.0280.0130.0600.0390.0280.0140.0130.0150.0140.0150.0140.0130.0140.0140.1800.0130.0130.0130.0140.0460.0140.0140.0670.072
pain_during_bowel_movements0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.9420.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.6570.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.0930.0130.0130.0140.0270.0130.0130.0130.0460.9420.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0141.0000.9420.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
pain_in_anal_region0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.9420.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.6570.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.0930.0130.0130.0140.0270.0130.0130.0130.0460.9420.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.9421.0000.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
painful_walking0.1110.0450.0430.0270.0270.0270.0440.0270.0260.0270.0270.0280.0270.0570.0670.0280.0270.0430.0870.0950.0270.0280.0280.0440.0270.0430.0760.0270.0770.0260.0450.0760.0260.0270.0560.0270.0280.0680.0260.0440.0450.1760.0270.0250.1190.1350.6570.0270.0280.0430.0270.0270.0270.0690.0270.0860.2110.6570.0270.0670.1200.0570.0280.0870.0580.0440.6570.0270.0690.0260.4390.1190.4460.0260.0440.0280.0270.0271.0000.0280.0270.0260.0580.0280.3040.0270.0270.0260.0280.0280.0270.0450.0280.0440.0280.0280.0260.0260.0270.0280.0270.0940.0280.0270.0260.0260.4460.0280.0430.0260.0860.0570.9420.0270.0260.0280.0270.0280.0270.0260.0270.0270.1740.0260.0260.0260.0270.0670.0270.0270.0960.103
palpitations0.0790.0290.0280.0140.0140.9700.0280.0140.0130.0140.0140.0150.0140.5450.0460.0150.0140.0270.0610.0660.0140.0150.0150.0280.0140.0280.0530.0140.0530.0130.0290.0530.0130.0140.0380.9700.0150.4890.0130.0280.0290.1790.0140.0120.2680.0950.0140.0140.0150.0280.0140.0140.0140.4820.0140.0600.0600.0140.0140.0460.0850.0380.0150.0610.0390.0280.0140.0140.0470.0130.0290.2660.0280.0130.0280.0150.0140.0140.0281.0000.0140.0130.0390.0150.3190.0140.0140.0130.0150.0150.0140.0290.0150.0280.0150.0150.0130.0130.0140.0150.0140.0660.9960.0140.0130.0130.0280.0150.0280.0130.3700.0390.0280.0140.0130.0150.0140.0150.0140.0130.0140.0140.1800.0130.0130.0130.0140.0460.0140.0140.0670.072
passage_of_gases0.2770.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.0930.0130.0130.0140.6270.0130.9420.0130.0460.0130.0580.0580.0130.0130.0450.2380.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0141.0000.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1740.0120.0120.0120.0130.0450.0130.0130.0650.070
patches_in_throat0.0740.0270.0260.0120.0120.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0500.0110.0270.0500.0110.0120.0350.0120.0130.0440.8820.0260.0270.1180.0120.0090.0790.2220.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.8820.0270.0800.0260.0110.0260.0130.0120.0120.0260.0130.0121.0000.0360.0130.4540.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.0110.0120.0130.0120.0620.0130.0120.0110.0110.0260.0130.0260.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1170.0110.0110.0110.0120.0430.0120.0120.0630.068
phlegm0.1420.0600.0570.0380.0380.0380.0580.0380.0360.0380.0380.5650.0380.0730.5160.0390.0380.0560.6850.5930.0380.0390.5650.0580.0380.3690.7280.0380.0990.0360.0590.0980.0360.0380.0720.0380.0390.0870.0360.0580.3570.3160.0380.0350.0580.4170.0380.0380.0390.0570.0380.0380.0380.0880.0380.1090.1100.0380.0380.0860.0670.0730.5650.6540.2860.0580.0380.0380.2270.0360.0590.1520.0580.0360.0580.0390.0380.0380.0580.0390.0380.0361.0000.0390.4150.0380.0380.0360.0390.0390.0380.0590.5650.0580.5650.5350.0360.0360.0380.5650.0380.1190.0390.0380.0360.0360.0580.0390.0570.0360.3940.6390.0580.0380.0360.0390.0380.5650.0380.0360.0380.0380.0350.0360.0360.0360.0380.2180.0380.0380.1280.131
polyuria0.0790.0290.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.5450.0460.0150.0140.0270.0610.0660.0140.0150.0150.0280.0140.0280.0530.0140.0530.0130.0290.0530.0130.0140.0380.0140.0150.4620.0130.0280.0290.1790.0140.0120.0840.0950.0140.0140.9960.0280.0140.0140.9700.0470.0140.0600.0600.0140.0140.4650.0850.0380.0150.0610.0390.0280.0140.0140.0470.0130.0290.0840.0280.0130.6760.0150.0140.0140.0280.0150.0140.0130.0391.0000.3470.0140.0140.0130.0150.0150.0140.0290.0150.6760.0150.0150.0130.0130.0140.0150.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.0600.0390.0280.0140.0130.0150.0140.0150.0140.0130.0140.0140.1240.0130.0130.0130.0140.4650.0140.0140.0670.072
prognosis0.3030.4590.3000.3110.3380.3110.4850.3380.4540.3380.3380.3470.3380.2530.3480.3190.3380.2960.3180.4160.3110.3190.3470.3040.3380.3730.3760.3380.4130.3280.2930.2810.3280.4670.3730.3110.3190.3250.4540.2890.2940.5980.4670.3190.4680.4320.3380.4670.3470.4780.3380.3380.3380.4930.3380.3080.2950.3380.3110.3200.4450.5960.3470.4530.4210.4460.3380.3380.3530.4540.2930.4760.3040.3280.3040.3470.3380.3380.3040.3190.3380.4540.4150.3471.0000.3380.3110.4540.3190.3190.3380.4910.3470.2890.3470.3470.4540.4540.3380.3470.3380.2720.3190.3380.9470.3280.3040.3190.4780.3280.2960.4090.3040.4670.3280.3190.3380.3470.3380.3280.9740.3380.2590.4540.3280.3280.3110.2040.3380.3110.5000.402
prominent_veins_on_calf0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.9420.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.9420.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1730.0130.0100.0810.0930.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.6570.0140.0130.0130.0270.0140.0130.0120.0380.0140.3381.0000.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.9130.0140.9420.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
puffy_face_and_eyes0.0760.6770.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.9700.0130.0260.0590.0640.9420.0140.0140.0270.0130.0270.0510.0130.0520.0120.6860.0510.0120.0130.5340.0130.9700.0450.0120.0270.0280.1560.0130.0100.0810.0930.0130.0130.0140.0270.0130.0130.0130.4690.0130.0580.0580.0130.0130.4510.0820.0370.0140.0590.0380.6570.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3110.0131.0000.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.9700.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.9420.0450.0130.0130.0650.070
pus_filled_pimples0.0740.0270.0260.0120.0120.0120.0260.0120.8820.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0500.0110.0270.0500.0110.0120.0350.0120.0130.0440.0110.0260.0270.1180.0120.0090.0790.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.0260.0110.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.4540.0120.0121.0000.0130.0130.0120.0270.0130.0260.0130.0130.8820.0110.0120.0130.0120.3190.0130.0120.0110.0110.0260.0130.0260.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1170.0110.0110.0110.0120.0430.0120.0120.0630.068
receiving_blood_transfusion0.2860.0290.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.0460.0150.0140.0270.0610.0660.0140.0150.0150.0280.0140.0280.0530.0140.4100.0130.0290.0530.0130.0140.0380.0140.0150.0470.0130.0280.0290.1790.0140.0120.0840.0950.0140.0140.0150.0280.0140.0140.0140.0470.0140.3700.0600.0140.0140.4650.2650.0380.0150.3850.0390.0280.0140.0140.0470.0130.0290.0840.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.0390.0150.3190.0140.0140.0131.0000.9960.0140.0290.0150.0280.0150.0150.0130.0130.0140.0150.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.0600.0390.0280.0140.0130.0150.0140.0150.0140.0130.0140.0140.1240.0130.0130.0130.0140.0460.0140.9700.3530.309
receiving_unsterile_injections0.2860.0290.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.0460.0150.0140.0270.0610.0660.0140.0150.0150.0280.0140.0280.0530.0140.4100.0130.0290.0530.0130.0140.0380.0140.0150.0470.0130.0280.0290.1790.0140.0120.0840.0950.0140.0140.0150.0280.0140.0140.0140.0470.0140.3700.0600.0140.0140.4650.2650.0380.0150.3850.0390.0280.0140.0140.0470.0130.0290.0840.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.0390.0150.3190.0140.0140.0130.9961.0000.0140.0290.0150.0280.0150.0150.0130.0130.0140.0150.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.0600.0390.0280.0140.0130.0150.0140.0150.0140.0130.0140.0140.1240.0130.0130.0130.0140.0460.0140.9700.3530.309
red_sore_around_nose0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.9420.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.1930.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0141.0000.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.3290.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.9420.0130.0650.070
red_spots_over_body0.1130.0460.0440.0280.0280.0280.4660.0280.0270.0280.0280.0290.0280.0570.0680.0290.0280.0430.0880.1800.0280.0290.0290.0450.0280.0440.0780.0280.0780.0270.0460.0780.0270.0280.0570.0280.0290.0690.0270.0450.0460.2530.0280.0250.3930.3340.0280.0280.0290.0440.0280.0280.0280.0700.0280.2250.2060.0280.0280.3020.3890.0570.0290.5300.3570.0450.0280.0280.2750.0270.0460.1330.0450.0270.0450.6670.0280.0280.0450.0290.0280.0270.0590.0290.4910.0280.0280.0270.0290.0290.0281.0000.0290.0450.0290.0290.0270.0270.0280.0290.0280.4800.0290.0280.0270.0270.0450.0290.0440.0270.0870.3610.0450.0280.0270.0290.0280.0290.0280.0270.0280.0280.0290.0270.0270.0270.0280.0680.0280.0280.0970.104
redness_of_eyes0.0790.0290.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.0460.0150.0140.0270.3870.3360.0140.0150.9960.0280.0140.6860.4120.0140.0530.0130.0290.0530.0130.0140.0380.0140.0150.0470.0130.0280.0290.1790.0140.0120.2680.2360.0140.0140.0150.0280.0140.0140.0140.0470.0140.0600.0600.0140.0140.0460.0850.0380.9960.3630.0390.0280.0140.0140.4820.0130.0290.0840.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.5650.0150.3470.0140.0140.0130.0150.0150.0140.0291.0000.0280.9960.0150.0130.0130.0140.9960.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.0600.5400.0280.0140.0130.0150.0140.9960.0140.0130.0140.0140.1240.0130.0130.0130.0140.0460.0140.0140.0670.072
restlessness0.1110.4590.0430.0270.0270.0270.0440.0270.0260.0270.0270.0280.0270.3480.0670.0280.0270.0430.0870.0950.0270.0280.0280.0440.0270.0430.0760.0270.0770.0260.0450.2470.0260.0270.0560.0270.0280.6430.0260.0440.4390.2490.0270.0250.1190.1350.0270.0270.6760.0430.0270.0270.6570.3000.0270.0860.0860.0270.0270.2880.1200.0570.0280.0870.0580.4460.0270.0270.0690.0260.4660.1190.0440.0260.4460.0280.0270.0270.0440.0280.0270.0260.0580.6760.2890.0270.0270.0260.0280.0280.0270.0450.0281.0000.0280.0280.0260.0260.0270.0280.0270.0940.0280.0270.0260.0260.0440.0280.0430.0260.2130.0570.0440.0270.0260.0280.0270.0280.0270.0260.0270.0270.1740.0260.0260.0260.0270.6480.0270.0270.0960.103
runny_nose0.0790.0290.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.0460.0150.0140.0270.3870.3360.0140.0150.9960.0280.0140.6860.4120.0140.0530.0130.0290.0530.0130.0140.0380.0140.0150.0470.0130.0280.0290.1790.0140.0120.2680.2360.0140.0140.0150.0280.0140.0140.0140.0470.0140.0600.0600.0140.0140.0460.0850.0380.9960.3630.0390.0280.0140.0140.4820.0130.0290.0840.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.5650.0150.3470.0140.0140.0130.0150.0150.0140.0290.9960.0281.0000.0150.0130.0130.0140.9960.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.0600.5400.0280.0140.0130.0150.0140.9960.0140.0130.0140.0140.1240.0130.0130.0130.0140.0460.0140.0140.0670.072
rusty_sputum0.0790.0290.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.4680.0150.0140.0270.3870.3360.0140.0150.0150.0280.0140.0280.4120.0140.0530.0130.0290.0530.0130.0140.0380.0140.0150.0470.0130.0280.7040.1790.0140.0120.0840.2360.0140.0140.0150.0280.0140.0140.0140.0470.0140.0600.0600.0140.0140.0460.0850.0380.0150.3630.0390.0280.0140.0140.0470.0130.0290.0840.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.5350.0150.3470.0140.0140.0130.0150.0150.0140.0290.0150.0280.0151.0000.0130.0130.0140.0150.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.3700.0390.0280.0140.0130.0150.0140.0150.0140.0130.0140.0140.1240.0130.0130.0130.0140.0460.0140.0140.0670.072
scurring0.0740.0270.0260.0120.0120.0120.0260.0120.8820.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0500.0110.0270.0500.0110.0120.0350.0120.0130.0440.0110.0260.0270.1180.0120.0090.0790.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.0260.0110.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.4540.0120.0120.8820.0130.0130.0120.0270.0130.0260.0130.0131.0000.0110.0120.0130.0120.3190.0130.0120.0110.0110.0260.0130.0260.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1170.0110.0110.0110.0120.0430.0120.0120.0630.068
shivering0.0740.0270.0260.0120.0120.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.2930.0120.0130.0130.0260.0120.6060.0500.0120.0500.0110.0270.0500.0110.0120.0350.0120.0130.0440.0110.0260.0270.1180.0120.0090.0790.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.0260.0110.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.4540.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0111.0000.0120.0130.0120.0620.0130.0120.0110.0110.0260.0130.0260.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1170.8820.0110.0110.0120.0430.0120.0120.0630.068
silver_like_dusting0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.0930.0130.0130.0140.0270.9420.0130.0130.0460.0130.0580.3580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0121.0000.0140.9420.3290.0140.9420.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
sinus_pressure0.0790.0290.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.0460.0150.0140.0270.3870.3360.0140.0150.9960.0280.0140.6860.4120.0140.0530.0130.0290.0530.0130.0140.0380.0140.0150.0470.0130.0280.0290.1790.0140.0120.2680.2360.0140.0140.0150.0280.0140.0140.0140.0470.0140.0600.0600.0140.0140.0460.0850.0380.9960.3630.0390.0280.0140.0140.4820.0130.0290.0840.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.5650.0150.3470.0140.0140.0130.0150.0150.0140.0290.9960.0280.9960.0150.0130.0130.0141.0000.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.0600.5400.0280.0140.0130.0150.0140.9960.0140.0130.0140.0140.1240.0130.0130.0130.0140.0460.0140.0140.0670.072
skin_peeling0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.0930.0130.0130.0140.0270.9420.0130.0130.0460.0130.0580.3580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.9420.0141.0000.3290.0140.9420.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
skin_rash0.2230.0960.0920.0640.0640.0640.2030.0640.3190.0640.3290.0660.0640.1170.1370.0660.0640.1650.1760.0250.0640.0660.0660.0940.0640.0920.1550.0640.1560.0620.0950.1550.2960.0640.1160.0640.0660.1390.0620.0940.0950.1030.0640.0600.0510.1160.0640.0640.0660.0920.3290.0640.0640.1410.0640.3180.1700.0640.0640.0650.0470.1170.0660.1730.1090.0940.0640.0640.0580.0620.0950.0890.0940.2960.0940.3390.0640.0640.0940.0660.0640.0620.1190.0660.2720.0640.0640.3190.0660.0660.3290.4800.0660.0940.0660.0660.3190.0620.3290.0660.3291.0000.0660.3290.0620.2960.0940.0660.1600.0620.1730.1120.0940.0640.0620.0660.0640.0660.0640.0620.0640.0640.2240.0620.0620.0620.0640.1380.3290.0640.1930.207
slurred_speech0.0790.0290.0280.0140.0140.9700.0280.0140.0130.0140.0140.0150.0140.5450.0460.0150.0140.0270.0610.0660.0140.0150.0150.0280.0140.0280.0530.0140.0530.0130.0290.0530.0130.0140.0380.9700.0150.4890.0130.0280.0290.1790.0140.0120.2680.0950.0140.0140.0150.0280.0140.0140.0140.4820.0140.0600.0600.0140.0140.0460.0850.0380.0150.0610.0390.0280.0140.0140.0470.0130.0290.2660.0280.0130.0280.0150.0140.0140.0280.9960.0140.0130.0390.0150.3190.0140.0140.0130.0150.0150.0140.0290.0150.0280.0150.0150.0130.0130.0140.0150.0140.0661.0000.0140.0130.0130.0280.0150.0280.0130.3700.0390.0280.0140.0130.0150.0140.0150.0140.0130.0140.0140.1800.0130.0130.0130.0140.0460.0140.0140.0670.072
small_dents_in_nails0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.0930.0130.0130.0140.0270.9420.0130.0130.0460.0130.0580.3580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.9420.0140.9420.3290.0141.0000.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
spinning_movements0.0740.0270.0260.0120.0120.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0500.0110.0270.0500.0110.0120.0350.0120.0130.0440.0110.0260.0270.1180.0120.0090.2520.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.5130.0130.0570.0360.0260.0120.0120.0440.0110.0270.2500.0260.0110.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.9470.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.0110.0120.0130.0120.0620.0130.0121.0000.0110.0260.0130.0260.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.9130.0120.1690.0110.0110.0110.0120.0430.0120.0120.0630.068
spotting_ urination0.0740.0270.0260.0120.0120.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.6140.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0500.0110.0270.0500.0110.0120.0350.0120.0130.0440.0110.0260.0270.1180.0120.0090.0790.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.3480.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.0260.0110.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.3280.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.0110.0120.0130.0120.2960.0130.0120.0111.0000.0260.0130.6060.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1170.0110.0110.0110.0120.0430.0120.0120.0630.068
stiff_neck0.1110.0450.4530.0270.0270.0270.0440.0270.0260.0270.0270.0280.0270.3480.0670.0280.0270.0430.0870.0950.0270.0280.0280.0440.0270.0430.0760.0270.0770.0260.4390.0760.0260.0270.0560.0270.0280.2850.0260.0440.0450.1760.0270.0250.1250.1350.0270.0270.0280.4530.0270.0270.0270.2800.0270.0860.0860.0270.0270.0670.1200.0570.0280.0870.0580.0440.6570.0270.0690.0260.4390.1190.0440.0260.0440.0280.0270.0270.4460.0280.0270.0260.0580.0280.3040.0270.0270.0260.0280.0280.0270.0450.0280.0440.0280.0280.0260.0260.0270.0280.0270.0940.0280.0270.0260.0261.0000.0280.0430.0260.0860.0570.4460.0270.0260.0280.0270.0280.0270.0260.0270.6570.1740.0260.0260.0260.0270.0670.0270.0270.0960.103
stomach_bleeding0.3050.0290.0280.9700.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.0460.0150.0140.0270.0610.0660.0140.9960.0150.0280.0140.0280.0530.0140.4100.0130.0290.0530.0130.0140.0380.0140.0150.0470.0130.0280.0290.1790.0140.0120.0840.2360.0140.0140.0150.0280.0140.0140.0140.0470.0140.0600.3680.0140.0140.0460.2840.0380.0150.0610.0390.0280.0140.0140.0470.0130.0290.2660.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.0390.0150.3190.0140.0140.0130.0150.0150.0140.0290.0150.0280.0150.0150.0130.0130.0140.0150.0140.0660.0150.0140.0130.0130.0281.0000.0280.0130.0600.0390.0280.0140.0130.0150.0140.0150.0140.0130.0140.0140.1800.0130.0130.0130.0140.0460.0140.0140.3530.309
stomach_pain0.1100.0450.4310.0270.0270.0270.0430.0270.0260.0270.0270.0280.0270.0560.0660.0280.0270.4100.2130.0930.0270.0280.0280.0430.0270.0430.2520.0270.0760.0260.0440.0750.0260.0270.0550.0270.0280.0670.0260.0430.0440.1730.0270.0240.1170.1330.0270.0270.0280.0430.0270.0270.0270.0680.0270.2010.0850.0270.0270.0660.1180.0560.0280.0860.0570.0430.0270.0270.0680.0260.0440.1180.0430.0260.0430.0280.0270.0270.0430.0280.0270.0260.0570.0280.4780.0270.0270.0260.0280.0280.0270.0440.0280.0430.0280.0280.0260.0260.0270.0280.0270.1600.0280.0270.0260.6060.0430.0281.0000.0260.0840.0560.0430.0270.0260.0280.0270.0280.0270.6460.0270.0270.0270.0260.0260.0260.0270.0660.0270.0270.0950.101
sunken_eyes0.0740.0270.0260.0120.0120.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0500.8820.0270.3880.0110.0120.0350.0120.0130.0440.0110.0260.0270.1180.0120.0090.0790.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.0260.0110.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.3280.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.0110.0120.0130.0120.0620.0130.0120.0110.0110.0260.0130.0261.0000.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1520.0110.0110.0110.0120.0430.0120.0120.0630.068
sweating0.2050.2200.0840.0580.0580.3600.0860.0580.0560.0580.0580.3700.0580.1390.5100.0600.0580.0830.3950.3410.0580.0600.0600.0860.0580.0840.2550.0580.1430.0560.0870.2440.0560.0580.1060.3600.0600.3190.0560.0860.5240.1990.0580.0540.0820.1780.0580.0580.0600.0840.0580.0580.0580.3240.0580.1580.1590.0580.0580.1260.0600.1070.0600.2100.1470.2130.0580.0580.0950.0560.2250.0790.0860.0560.0860.0600.0580.0580.0860.3700.0580.0560.3940.0600.2960.0580.0580.0560.0600.0600.0580.0870.0600.2130.0600.3700.0560.0560.0580.0600.0580.1730.3700.0580.0560.0560.0860.0600.0840.0561.0000.1500.0860.0580.0560.0600.0580.0600.0580.0560.0580.0580.1880.0560.0560.0560.0580.3100.0580.0580.0000.189
swelled_lymph_nodes0.1400.0590.0560.0370.0370.0370.0570.0370.0360.0370.0370.5700.0370.0720.2240.0390.0370.0550.4190.3550.0370.0390.5400.0570.0370.3500.4520.0370.0980.0360.0580.0970.0360.0370.0720.0370.0390.0860.0360.0570.0580.3130.0370.0350.2540.4130.0370.0370.0390.0560.0370.0370.0370.0880.0370.1360.1090.0370.0370.2050.2730.0720.5400.6610.6210.0570.0370.0370.2140.0360.0580.1500.0570.0360.0570.0390.0370.0370.0570.0390.0370.0360.6390.0390.4090.0370.0370.0360.0390.0390.0370.3610.5400.0570.5400.0390.0360.0360.0370.5400.0370.1120.0390.0370.0360.0360.0570.0390.0560.0360.1501.0000.0570.0370.0360.0390.0370.5400.0370.0360.0370.0370.0310.0360.0360.0360.0370.2220.0370.0370.1310.130
swelling_joints0.1110.0450.0430.0270.0270.0270.0440.0270.0260.0270.0270.0280.0270.0570.0670.0280.0270.0430.0870.0950.0270.0280.0280.0440.0270.0430.0760.0270.0770.0260.0450.0760.0260.0270.0560.0270.0280.0680.0260.0440.0450.1760.0270.0250.1190.1350.6570.0270.0280.0430.0270.0270.0270.0690.0270.0860.2110.6570.0270.0670.1200.0570.0280.0870.0580.0440.6570.0270.0690.0260.4390.1190.4460.0260.0440.0280.0270.0270.9420.0280.0270.0260.0580.0280.3040.0270.0270.0260.0280.0280.0270.0450.0280.0440.0280.0280.0260.0260.0270.0280.0270.0940.0280.0270.0260.0260.4460.0280.0430.0260.0860.0571.0000.0270.0260.0280.0270.0280.0270.0260.0270.0270.1740.0260.0260.0260.0270.0670.0270.0270.0960.103
swelling_of_stomach0.2770.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.9420.0360.0130.0140.0450.0120.0270.0280.1220.9420.0100.0810.0930.0130.9420.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.4670.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0271.0000.0120.0140.0130.0140.0130.0120.0130.0130.1740.0120.0120.0120.0130.0450.0130.0130.0650.300
swollen_blood_vessels0.0740.0270.0260.0120.0120.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.9130.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.9130.0500.0110.0270.0500.0110.0120.0350.0120.0130.0440.0110.0260.0270.1670.0120.0090.0790.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.0260.0110.6370.0130.0120.0120.0260.0130.0120.0110.0360.0130.3280.9130.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.0110.0120.0130.0120.0620.0130.0120.0110.0110.0260.0130.0260.0110.0560.0360.0260.0121.0000.0130.9130.0130.0120.0110.0120.0120.1170.0110.0110.0110.0120.0430.0120.0120.0630.068
swollen_extremeties0.0790.6950.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.0460.9960.0140.0270.0610.0660.9700.0150.0150.0280.0140.0280.0530.0140.0530.0130.7040.0530.0130.0140.5500.0140.9960.0470.0130.0280.0290.1620.0140.0120.0840.0950.0140.0140.0150.0280.0140.0140.0140.4820.0140.0600.0600.0140.0140.4650.0850.0380.0150.0610.0390.6760.0140.0140.0470.0130.0290.0840.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.0390.0150.3190.0140.9700.0130.0150.0150.0140.0290.0150.0280.0150.0150.0130.0130.0140.0150.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.0600.0390.0280.0140.0131.0000.0140.0150.0140.0130.0140.0140.1240.0130.0130.0130.9700.0460.0140.0140.0670.072
swollen_legs0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.9420.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.9420.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1730.0130.0100.0810.0930.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.6570.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.9420.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.9130.0141.0000.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
throat_irritation0.0790.0290.0280.0140.0140.0140.0280.0140.0130.0140.0140.0150.0140.0380.0460.0150.0140.0270.3870.3360.0140.0150.9960.0280.0140.6860.4120.0140.0530.0130.0290.0530.0130.0140.0380.0140.0150.0470.0130.0280.0290.1790.0140.0120.2680.2360.0140.0140.0150.0280.0140.0140.0140.0470.0140.0600.0600.0140.0140.0460.0850.0380.9960.3630.0390.0280.0140.0140.4820.0130.0290.0840.0280.0130.0280.0150.0140.0140.0280.0150.0140.0130.5650.0150.3470.0140.0140.0130.0150.0150.0140.0290.9960.0280.9960.0150.0130.0130.0140.9960.0140.0660.0150.0140.0130.0130.0280.0150.0280.0130.0600.5400.0280.0140.0130.0150.0141.0000.0140.0130.0140.0140.1240.0130.0130.0130.0140.0460.0140.0140.0670.072
toxic_look_(typhos)0.2770.0280.0270.0130.0130.0130.0270.9420.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.3480.0130.0140.0140.6570.0130.0270.0510.0130.0520.0120.0280.4000.0120.0130.0360.0130.0140.0450.0120.0270.0280.1900.0130.0100.2600.2470.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.2580.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0141.0000.0120.0130.0130.1740.0120.0120.0120.0130.0450.0130.0130.0650.070
ulcers_on_tongue0.0740.0270.6060.0120.0120.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.0250.3430.0620.0120.0130.0130.0260.0120.0260.3880.0120.0500.0110.0270.0500.0110.0120.0350.0120.0130.0440.0110.0260.0270.1180.0120.0090.0790.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.0260.0110.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.3280.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.0110.0120.0130.0120.0620.0130.0120.0110.0110.0260.0130.6460.0110.0560.0360.0260.0120.0110.0130.0120.0130.0121.0000.0120.0120.1520.0110.0110.0110.0120.0430.0120.0120.0630.068
unsteadiness0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.2600.0930.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.5290.0140.0590.0380.0270.0130.0130.0460.0120.0280.2580.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.9740.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.9130.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0121.0000.0130.1740.0120.0120.0120.0130.0450.0130.0130.0650.070
visual_disturbances0.0760.0280.6660.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.5290.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.6480.0510.0120.0130.0360.0130.0140.4480.0120.0270.0280.1220.0130.0100.2600.0930.0130.0130.0140.6660.0130.0130.0130.4420.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.6570.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0131.0000.1210.0120.0120.0120.0130.0450.0130.0130.0650.070
vomiting0.4790.1790.0120.1740.1580.1740.0470.1740.1170.1210.1210.1800.1210.0380.0390.1240.1210.1690.0540.1430.1210.1800.1240.0340.1210.1720.0000.1210.2730.1520.1770.2610.1170.1740.2150.1740.1240.0920.1170.1740.1770.0000.1740.1140.1980.1140.1210.1740.1240.0270.1210.1740.1210.0980.1210.0550.1990.1210.1210.2540.3140.0380.1240.0590.1440.1740.1210.1210.2130.1170.1770.5240.1740.1170.1740.1800.1210.1210.1740.1800.1740.1170.0350.1240.2590.1210.1210.1170.1240.1240.1210.0290.1240.1740.1240.1240.1170.1170.1210.1240.1210.2240.1800.1210.1690.1170.1740.1800.0270.1520.1880.0310.1740.1740.1170.1240.1210.1240.1740.1520.1740.1211.0000.1170.1170.1520.1210.0530.1210.1210.2690.314
watering_from_eyes0.0740.0270.0260.0120.0120.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.2930.0120.0130.0130.0260.0120.6060.0500.0120.0500.0110.0270.0500.0110.0120.0350.0120.0130.0440.0110.0260.0270.1180.0120.0090.0790.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.0260.0110.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.4540.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.8820.0120.0130.0120.0620.0130.0120.0110.0110.0260.0130.0260.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1171.0000.0110.0110.0120.0430.0120.0120.0630.068
weakness_in_limbs0.0740.0270.0260.0120.0120.0120.5970.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0500.0110.0270.0500.0110.0120.5170.0120.0130.0440.0110.0260.0270.1180.0120.0090.0790.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.5130.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.6370.0110.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.3280.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.0110.0120.0130.0120.0620.0130.0120.0110.0110.0260.0130.0260.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1170.0111.0000.0110.0120.0430.0120.0120.0630.068
weakness_of_one_body_side0.0740.0270.0260.0120.9130.0120.0260.0120.0110.0120.0120.0130.0120.0350.0430.0130.0120.0250.0570.0620.0120.0130.0130.0260.0120.0260.0500.0120.0500.0110.0270.0500.0110.0120.0350.0120.0130.0440.0110.0260.0270.1180.0120.0090.2320.0900.0120.0120.0130.0260.0120.0120.0120.0440.0120.0560.0560.0120.0120.0430.0800.0350.0130.0570.0360.0260.0120.0120.0440.0110.0270.0800.0260.0110.0260.0130.0120.0120.0260.0130.0120.0110.0360.0130.3280.0120.0120.0110.0130.0130.0120.0270.0130.0260.0130.0130.0110.0110.0120.0130.0120.0620.0130.0120.0110.0110.0260.0130.0260.0110.0560.0360.0260.0120.0110.0130.0120.0130.0120.0110.0120.0120.1520.0110.0111.0000.0120.0430.0120.0120.0630.068
weight_gain0.0760.6770.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.9700.0130.0260.0590.0640.9420.0140.0140.0270.0130.0270.0510.0130.0520.0120.6860.0510.0120.0130.5340.0130.9700.0450.0120.0270.0280.1560.0130.0100.0810.0930.0130.0130.0140.0270.0130.0130.0130.4690.0130.0580.0580.0130.0130.4510.0820.0370.0140.0590.0380.6570.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3110.0130.9420.0120.0140.0140.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.9700.0130.0140.0130.0120.0130.0130.1210.0120.0120.0121.0000.0450.0130.0130.0650.070
weight_loss0.0150.2970.0660.0450.0450.0450.0670.0450.0430.0450.0450.4650.0450.2080.1590.0460.0450.0650.0980.0620.0450.0460.0460.0670.0450.0660.1210.0450.1190.0430.0680.1210.0430.0450.0840.0450.0460.4150.0430.0670.2820.3620.0450.0420.1740.1390.0450.0450.4650.0660.0450.0450.4510.1650.0450.0900.1270.0450.0450.1570.0000.0850.0460.0960.2180.2880.0450.0450.1020.0430.3020.1750.0670.0430.2880.0460.0450.0450.0670.0460.0450.0430.2180.4650.2040.0450.0450.0430.0460.0460.0450.0680.0460.6480.0460.0460.0430.0430.0450.0460.0450.1380.0460.0450.0430.0430.0670.0460.0660.0430.3100.2220.0670.0450.0430.0460.0450.0460.0450.0430.0450.0450.0530.0430.0430.0430.0451.0000.0450.0450.0700.039
yellow_crust_ooze0.0760.0280.0270.0130.0130.0130.0270.0130.0120.0130.9420.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.0520.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1220.0130.0100.0810.1930.0130.0130.0140.0270.0130.0130.0130.0460.0130.0580.0580.0130.0130.0450.0820.0370.0140.0590.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3380.0130.0130.0120.0140.0140.9420.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.3290.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0451.0000.0130.0650.070
yellow_urine0.2770.0280.0270.0130.0130.0130.0270.0130.0120.0130.0130.0140.0130.0370.0440.0140.0130.0260.0590.0640.0130.0140.0140.0270.0130.0270.0510.0130.3980.0120.0280.0510.0120.0130.0360.0130.0140.0450.0120.0270.0280.1730.0130.0100.0810.0930.0130.0130.0140.0270.0130.0130.0130.0460.0130.3600.0580.0130.0130.4510.2570.0370.0140.3750.0380.0270.0130.0130.0460.0120.0280.0820.0270.0120.0270.0140.0130.0130.0270.0140.0130.0120.0380.0140.3110.0130.0130.0120.9700.9700.0130.0280.0140.0270.0140.0140.0120.0120.0130.0140.0130.0640.0140.0130.0120.0120.0270.0140.0270.0120.0580.0370.0270.0130.0120.0140.0130.0140.0130.0120.0130.0130.1210.0120.0120.0120.0130.0450.0131.0000.3430.300
yellowing_of_eyes0.5260.0990.0950.3430.0650.0650.0960.0650.0630.0650.0650.3530.0650.1200.0720.0670.0650.0930.0000.0220.0650.3530.0670.0960.0650.0950.0310.0650.6060.0630.0970.0310.0630.0650.1190.0650.0670.1420.0630.1650.0970.2580.0650.0610.2430.0000.0650.0650.0670.0950.0650.0650.0650.1440.0650.1720.3500.0650.0650.0700.7670.1200.0670.1920.3820.0960.0650.0650.0740.0630.0970.4590.0960.0630.0960.0670.0650.0650.0960.0670.0650.0630.1280.0670.5000.0650.0650.0630.3530.3530.0650.0970.0670.0960.0670.0670.0630.0630.0650.0670.0650.1930.0670.0650.0630.0630.0960.3530.0950.0630.0000.1310.0960.0650.0630.0670.0650.0670.0650.0630.0650.0650.2690.0630.0630.0630.0650.0700.0650.3431.0000.715
yellowish_skin0.7320.1060.1010.3000.0700.0700.1030.0700.0680.0700.0700.0720.0700.1290.1500.0720.0700.1000.1920.2090.0700.3090.0720.1030.0700.1010.1700.0700.7090.0680.1040.0000.0680.3000.1270.0700.0720.1520.0680.1620.1040.1940.3000.0660.2600.0400.0700.3000.0720.1010.0700.0700.0700.1540.0700.3000.2970.0700.0700.0390.5430.1290.0720.0190.0960.1030.0700.0700.0430.0680.1040.4050.1030.0680.1030.0720.0700.0700.1030.0720.0700.0680.1310.0720.4020.0700.0700.0680.3090.3090.0700.1040.0720.1030.0720.0720.0680.0680.0700.0720.0700.2070.0720.0700.0680.0680.1030.3090.1010.0680.1890.1300.1030.3000.0680.0720.0700.0720.0700.0680.0700.0700.3140.0680.0680.0680.0700.0390.0700.3000.7151.000

Missing values

2024-11-13T16:26:32.027665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-13T16:26:33.305754image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

itchingskin_rashnodal_skin_eruptionscontinuous_sneezingshiveringchillsjoint_painstomach_painacidityulcers_on_tonguemuscle_wastingvomitingburning_micturitionspotting_ urinationfatigueweight_gainanxietycold_hands_and_feetsmood_swingsweight_lossrestlessnesslethargypatches_in_throatirregular_sugar_levelcoughhigh_feversunken_eyesbreathlessnesssweatingdehydrationindigestionheadacheyellowish_skindark_urinenausealoss_of_appetitepain_behind_the_eyesback_painconstipationabdominal_paindiarrhoeamild_feveryellow_urineyellowing_of_eyesacute_liver_failurefluid_overloadswelling_of_stomachswelled_lymph_nodesmalaiseblurred_and_distorted_visionphlegmthroat_irritationredness_of_eyessinus_pressurerunny_nosecongestionchest_painweakness_in_limbsfast_heart_ratepain_during_bowel_movementspain_in_anal_regionbloody_stoolirritation_in_anusneck_paindizzinesscrampsbruisingobesityswollen_legsswollen_blood_vesselspuffy_face_and_eyesenlarged_thyroidbrittle_nailsswollen_extremetiesexcessive_hungerextra_marital_contactsdrying_and_tingling_lipsslurred_speechknee_painhip_joint_painmuscle_weaknessstiff_neckswelling_jointsmovement_stiffnessspinning_movementsloss_of_balanceunsteadinessweakness_of_one_body_sideloss_of_smellbladder_discomfortfoul_smell_of urinecontinuous_feel_of_urinepassage_of_gasesinternal_itchingtoxic_look_(typhos)depressionirritabilitymuscle_painaltered_sensoriumred_spots_over_bodybelly_painabnormal_menstruationdischromic _patcheswatering_from_eyesincreased_appetitepolyuriafamily_historymucoid_sputumrusty_sputumlack_of_concentrationvisual_disturbancesreceiving_blood_transfusionreceiving_unsterile_injectionscomastomach_bleedingdistention_of_abdomenhistory_of_alcohol_consumptionfluid_overload.1blood_in_sputumprominent_veins_on_calfpalpitationspainful_walkingpus_filled_pimplesblackheadsscurringskin_peelingsilver_like_dustingsmall_dents_in_nailsinflammatory_nailsblisterred_sore_around_noseyellow_crust_oozeprognosis
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61NaN100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000052
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911.0100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000052
itchingskin_rashnodal_skin_eruptionscontinuous_sneezingshiveringchillsjoint_painstomach_painacidityulcers_on_tonguemuscle_wastingvomitingburning_micturitionspotting_ urinationfatigueweight_gainanxietycold_hands_and_feetsmood_swingsweight_lossrestlessnesslethargypatches_in_throatirregular_sugar_levelcoughhigh_feversunken_eyesbreathlessnesssweatingdehydrationindigestionheadacheyellowish_skindark_urinenausealoss_of_appetitepain_behind_the_eyesback_painconstipationabdominal_paindiarrhoeamild_feveryellow_urineyellowing_of_eyesacute_liver_failurefluid_overloadswelling_of_stomachswelled_lymph_nodesmalaiseblurred_and_distorted_visionphlegmthroat_irritationredness_of_eyessinus_pressurerunny_nosecongestionchest_painweakness_in_limbsfast_heart_ratepain_during_bowel_movementspain_in_anal_regionbloody_stoolirritation_in_anusneck_paindizzinesscrampsbruisingobesityswollen_legsswollen_blood_vesselspuffy_face_and_eyesenlarged_thyroidbrittle_nailsswollen_extremetiesexcessive_hungerextra_marital_contactsdrying_and_tingling_lipsslurred_speechknee_painhip_joint_painmuscle_weaknessstiff_neckswelling_jointsmovement_stiffnessspinning_movementsloss_of_balanceunsteadinessweakness_of_one_body_sideloss_of_smellbladder_discomfortfoul_smell_of urinecontinuous_feel_of_urinepassage_of_gasesinternal_itchingtoxic_look_(typhos)depressionirritabilitymuscle_painaltered_sensoriumred_spots_over_bodybelly_painabnormal_menstruationdischromic _patcheswatering_from_eyesincreased_appetitepolyuriafamily_historymucoid_sputumrusty_sputumlack_of_concentrationvisual_disturbancesreceiving_blood_transfusionreceiving_unsterile_injectionscomastomach_bleedingdistention_of_abdomenhistory_of_alcohol_consumptionfluid_overload.1blood_in_sputumprominent_veins_on_calfpalpitationspainful_walkingpus_filled_pimplesblackheadsscurringskin_peelingsilver_like_dustingsmall_dents_in_nailsinflammatory_nailsblisterred_sore_around_noseyellow_crust_oozeprognosis
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Duplicate rows

Most frequently occurring

itchingskin_rashnodal_skin_eruptionscontinuous_sneezingshiveringchillsjoint_painstomach_painacidityulcers_on_tonguemuscle_wastingvomitingburning_micturitionspotting_ urinationfatigueweight_gainanxietycold_hands_and_feetsmood_swingsweight_lossrestlessnesslethargypatches_in_throatirregular_sugar_levelcoughhigh_feversunken_eyesbreathlessnesssweatingdehydrationindigestionheadacheyellowish_skindark_urinenausealoss_of_appetitepain_behind_the_eyesback_painconstipationabdominal_paindiarrhoeamild_feveryellow_urineyellowing_of_eyesacute_liver_failurefluid_overloadswelling_of_stomachswelled_lymph_nodesmalaiseblurred_and_distorted_visionphlegmthroat_irritationredness_of_eyessinus_pressurerunny_nosecongestionchest_painweakness_in_limbsfast_heart_ratepain_during_bowel_movementspain_in_anal_regionbloody_stoolirritation_in_anusneck_paindizzinesscrampsbruisingobesityswollen_legsswollen_blood_vesselspuffy_face_and_eyesenlarged_thyroidbrittle_nailsswollen_extremetiesexcessive_hungerextra_marital_contactsdrying_and_tingling_lipsslurred_speechknee_painhip_joint_painmuscle_weaknessstiff_neckswelling_jointsmovement_stiffnessspinning_movementsloss_of_balanceunsteadinessweakness_of_one_body_sideloss_of_smellbladder_discomfortfoul_smell_of urinecontinuous_feel_of_urinepassage_of_gasesinternal_itchingtoxic_look_(typhos)depressionirritabilitymuscle_painaltered_sensoriumred_spots_over_bodybelly_painabnormal_menstruationdischromic _patcheswatering_from_eyesincreased_appetitepolyuriafamily_historymucoid_sputumrusty_sputumlack_of_concentrationvisual_disturbancesreceiving_blood_transfusionreceiving_unsterile_injectionscomastomach_bleedingdistention_of_abdomenhistory_of_alcohol_consumptionfluid_overload.1blood_in_sputumprominent_veins_on_calfpalpitationspainful_walkingpus_filled_pimplesblackheadsscurringskin_peelingsilver_like_dustingsmall_dents_in_nailsinflammatory_nailsblisterred_sore_around_noseyellow_crust_oozeprognosis# duplicates
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